Final Assignment
Study Group 11: Nereid Kwok, Nisa Ozer, Lauren Wade, Thomas Giannetti-Fakhouri, Kazmer Nagy-Betegh, Chandrime Tolia
2021-09-21
1 Package Loading
2 Data Import
# Google Sheets permissions did not work with my google account
# use googlesheets4 to get data
#url <- "https://docs.google.com/spreadsheets/d/1IPS5dBSGtwYVbjsfbaMCYIWnOuRmJcbequohNxCyGVw/edit?resourcekey#gid=1625408792"
#googlesheets4::gs4_auth() # google sheets authorisation
# load "Ask a A Manager 2021 Survey" googlesheet
# https://www.askamanager.org/
# ask_a_manager_2021 <- googlesheets4::read_sheet(url) %>%
# janitor::clean_names()
# if googlesheets is now working, read local copy
ask_a_manager_2021 <- read_csv(here::here("data", "ask_a_manager_2021.csv"))%>%
janitor::clean_names()3 Intitial Data Exploration and Cleaning
skimr::skim(ask_a_manager_2021)| Name | ask_a_manager_2021 |
| Number of rows | 26765 |
| Number of columns | 18 |
| _______________________ | |
| Column type frequency: | |
| character | 14 |
| logical | 2 |
| numeric | 1 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| how_old_are_you | 0 | 1.00 | 5 | 10 | 0 | 7 | 0 |
| industry | 62 | 1.00 | 2 | 171 | 0 | 1084 | 0 |
| job_title | 0 | 1.00 | 1 | 126 | 0 | 12838 | 0 |
| additional_context_on_job_title | 19868 | 0.26 | 1 | 781 | 0 | 6612 | 0 |
| currency | 0 | 1.00 | 3 | 7 | 0 | 11 | 0 |
| additional_context_on_income | 23851 | 0.11 | 1 | 1143 | 0 | 2839 | 0 |
| country | 0 | 1.00 | 1 | 209 | 0 | 297 | 0 |
| state | 4761 | 0.82 | 4 | 114 | 0 | 125 | 0 |
| city | 23 | 1.00 | 1 | 171 | 0 | 4070 | 0 |
| overall_years_of_professional_experience | 0 | 1.00 | 9 | 16 | 0 | 8 | 0 |
| years_of_experience_in_field | 0 | 1.00 | 9 | 16 | 0 | 8 | 0 |
| highest_level_of_education_completed | 202 | 0.99 | 3 | 34 | 0 | 6 | 0 |
| gender | 155 | 0.99 | 3 | 29 | 0 | 5 | 0 |
| race | 151 | 0.99 | 5 | 125 | 0 | 47 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| other_monetary_comp | 26765 | 0 | NaN | : |
| currency_other | 26765 | 0 | NaN | : |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| annual_salary | 0 | 1 | 144988 | 5488158 | 0 | 54000 | 75712 | 110000 | 8.7e+08 | ▇▁▁▁▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| timestamp | 0 | 1 | 2021-04-27 11:02:09 | 2021-09-14 21:55:44 | 2021-04-28 12:35:21 | 23989 |
3.1 Clean Country Names
# unique(ask_a_manager_2021$country) # checking the list of unique country codes reveals that country names are inconsistent across the data
ask_a_manager_2021_cleaned <- ask_a_manager_2021%>%
mutate(
country = countrycode::countrycode(sourcevar = ask_a_manager_2021$country,
origin = 'country.name',
destination = 'country.name',
warn = TRUE,
nomatch = NULL)
)
ask_a_manager_2021_cleaned <- ask_a_manager_2021_cleaned%>%
mutate(
country = countrycode::countrycode(sourcevar = ask_a_manager_2021_cleaned$country,
origin = 'unicode.symbol',
destination = 'country.name',
warn = TRUE,
nomatch = NULL)
)
country_list <- unique(countryname_dict$country.name.en)
typos <- ask_a_manager_2021_cleaned%>%
filter(!country %in% unique(countryname_dict$country.name.en))
# unique(typos$country) #identifying the the typos and mismatches that the country code library couldn't deal with
typo_map <- hash() #create a map for the correct version of the typos
typo_map[["United Kingdom"]] <- list("Scotland",
"England",
"Northern Ireland",
"England/UK",
"England, UK",
"United Kindom",
"UK (Northern Ireland)", "UK for U.S. company",
"England, Gb",
"Englang",
"England, Gb",
"U.K. (northern England)",
"Unites kingdo",
"Unites kingdom",
"UK (England)",
"england",
"Jersey",
"Channel islands",
"UK, remote",
"Scotland, UK",
"England, UK.",
"Jersey, Channel islands",
"Wales, UK",
"Wales (UK)",
"UK, but for globally fully remote company",
"Wales"
)
typo_map[["United States"]] <- list("United State",
"ISA",
"America",
"United State of America",
"United Stated",
"USA-- Virgin Islands",
"United Statws" ,
"Unites States" ,
"U. S.",
"United Sates",
"Uniited States",
"Worldwide (based in US but short term trips aroudn the world)",
"United Sates of America",
"United States (I work from home and my clients are all over the US/Canada/PR",
"Unted States",
"United Statesp",
"United Stattes",
"United Statea",
"United Statees",
"Uniyed states",
"Uniyes States",
"United Status",
"Uniteed States",
"United Stares",
"Unites states",
"Unite States",
"The US",
"United statew",
"United Statues",
"Untied States",
"Unitied States",
"United Sttes",
"united stated",
"Uniter Statez",
"U. S" ,
"United Stateds",
"Usat",
"Unitef Stated",
"USaa",
"america",
"United States- Puerto Rico",
"California",
"Virginia",
"Hartford",
"San Francisco",
"USD",
"United Statss",
"I work for a UAE-based organization, though I am personally in the US.",
"United States",
"U.A.",
"UXZ",
"USS",
"IS",
"USAB",
"UA",
"I.S",
"I.S.",
"United y",
"UXZ"
)
typo_map[["Canada"]] <- list(
"Canadw",
"Can",
"Canda",
"Canada and USA",
"Csnada",
"Canad"
)
typo_map[["NA"]] <- list(
"Contracts",
"We don't get raises, we get quarterly bonuses, but they periodically asses income in the area you work, so I got a raise because a 3rd party assessment showed I was paid too little for the area we were located",
"Global" ,
"Currently finance",
"$2,175.84/year is deducted for benefits",
"Remote",
"bonus based on meeting yearly goals set w/ my supervisor",
"I earn commission on sales. If I meet quota, I'm guaranteed another 16k min. Last year i earned an additional 27k. It's not uncommon for people in my space to earn 100k+ after commission.",
"I was brought in on this salary to help with the EHR and very quickly was promoted to current position but compensation was not altered.",
"n/a (remote from wherever I want)",
"Africa",
"europe",
"na",
"Policy",
"NA",
"International",
"NZ",
"Y"
)
typo_map[["Spain"]] <- list("Catalonia")
typo_map[["Australia"]] <- list("Australi")
typo_map[["Denmark"]] <- list("Danmark")
typo_map[["Netherlands"]] <- list("NL",
"Nederland")
typo_map[["Panama"]] <- list("Panamá")
typo_map[["Brazil"]] <- list("Brasil")
typo_map[["Argentina"]] <- list("ARGENTINA BUT MY ORG IS IN THAILAND")
typo_map[["Mexico"]] <- list("México")
for (c in keys(typo_map))
{
ask_a_manager_2021_cleaned <- ask_a_manager_2021_cleaned%>%
mutate(country = case_when(country %in% values(typo_map, keys = c) ~c,
!country %in% values(typo_map, keys = c) ~ country))
}
ask_a_manager_2021_cleaned <- ask_a_manager_2021_cleaned%>%
mutate(country_code = countrycode::countrycode(sourcevar = ask_a_manager_2021_cleaned$country,
origin = 'country.name',
destination = 'iso3c',
nomatch = NA,
warn = TRUE))typos <- ask_a_manager_2021_cleaned%>%
filter(!country %in% unique(countryname_dict$country.name.en))
unique(typos$country)## [1] "NA"unique(ask_a_manager_2021_cleaned$country)## [1] "United States" "United Kingdom" "Canada"
## [4] "Netherlands" "Australia" "Spain"
## [7] "Finland" "France" "Germany"
## [10] "Ireland" "India" "Argentina"
## [13] "Denmark" "Switzerland" "Bermuda"
## [16] "Malaysia" "Mexico" "South Africa"
## [19] "Belgium" "Sweden" "Hong Kong SAR China"
## [22] "Kuwait" "Norway" "Sri Lanka"
## [25] "NA" "Greece" "Japan"
## [28] "Austria" "Brazil" "Hungary"
## [31] "Luxembourg" "Colombia" "New Zealand"
## [34] "Trinidad & Tobago" "Cayman Islands" "Czechia"
## [37] "Latvia" "Puerto Rico" "Rwanda"
## [40] "United Arab Emirates" "Bangladesh" "Romania"
## [43] "Serbia" "Philippines" "Russia"
## [46] "Poland" "Turkey" "Italy"
## [49] "China" "Afghanistan" "Israel"
## [52] "Taiwan" "Cambodia" "Vietnam"
## [55] "Singapore" "South Korea" "Thailand"
## [58] "Lithuania" "Eritrea" "Indonesia"
## [61] "Cuba" "Slovenia" "Côte d’Ivoire"
## [64] "Somalia" "Slovakia" "Portugal"
## [67] "Sierra Leone" "Bahamas" "Costa Rica"
## [70] "Chile" "Qatar" "Nigeria"
## [73] "Panama" "Congo - Brazzaville" "Uruguay"
## [76] "Pakistan" "Uganda" "Malta"
## [79] "Saudi Arabia" "Bulgaria" "Estonia"
## [82] "Morocco" "Ecuador" "Zimbabwe"
## [85] "Ghana" "Croatia" "Ukraine"
## [88] "Isle of Man" "Jamaica" "Kenya"
## [91] "Jordan"3.2 Clean Currency and Salary data
ask_a_manager_2021_cleaned$currency <- substr(ask_a_manager_2021_cleaned$currency, 0, 3)
annual_salary_stats <- mosaic::favstats(ask_a_manager_2021_cleaned$annual_salary)
# check if the selected currency matches the provided country
ask_a_manager_2021_cleaned <- ask_a_manager_2021_cleaned%>%
mutate(match_currency = countrycode(country, "country.name", "iso4217c"),
check_match = match_currency==currency)
# change the currency for the highest annual salaries if they don't match the countries currency as it is likely wrong
ask_a_manager_2021_cleaned <- ask_a_manager_2021_cleaned%>%
mutate(currency = case_when(annual_salary > annual_salary_stats$Q3 & check_match == F ~ match_currency,
TRUE ~ currency))
from <- unique(ask_a_manager_2021_cleaned$match_currency)
to <- c("USD")
exchange_rate <- getQuote(paste0(from, to, "=X"))
exchange_rate <- exchange_rate%>%
tibble::rownames_to_column(var = "index")
exchange_rate <- exchange_rate %>%
mutate(currency = substring(exchange_rate$index, first = 1, last =3)) %>%
select(index, currency, Last)%>%
clean_names()
ask_a_manager_2021_cleaned <-
merge(ask_a_manager_2021_cleaned, exchange_rate, by="currency", all.x = T)
ask_a_manager_2021_cleaned <- rename(ask_a_manager_2021_cleaned, exrate_to_USD = last)
ask_a_manager_2021_cleaned <- rename(ask_a_manager_2021_cleaned, exchange_matching_check = index)
#calculate usd salary
ask_a_manager_2021_cleaned <- ask_a_manager_2021_cleaned %>%
mutate(salary_usd = annual_salary * exrate_to_USD)%>%
clean_names()
usd_salary_stats <- mosaic::favstats(ask_a_manager_2021_cleaned$salary_usd)Check some of the lowest values within the dataset some of them likely left of 1000
potential_000missing <- ask_a_manager_2021_cleaned%>%
filter(salary_usd <= 999)
ask_a_manager_2021_cleaned <- ask_a_manager_2021_cleaned%>%
mutate(annual_salary = case_when(salary_usd < 1000 ~ annual_salary*1000,
TRUE ~ annual_salary),
salary_usd = case_when(salary_usd < 1000 ~ salary_usd*1000,
TRUE ~ salary_usd))
usd_salary_stats <- mosaic::favstats(ask_a_manager_2021_cleaned$salary_usd)3.3 Industry
standard_industry <- c("Accounting, Banking & Finance",
"Agriculture or Forestry",
"Art & Design",
"Business or Consulting",
"Computing or Tech",
"Education (Primary/Secondary)",
"Education (Higher Education)",
"Engineering or Manufacturing",
"Entertainment",
"Government and Public Administration",
"Health care",
"Hospitality & Events",
"Insurance",
"Law",
"Law Enforcement & Security",
"Leisure, Sport & Tourism",
"Marketing, Advertising & PR",
"Media & Digital",
"Nonprofits",
"Property or Construction",
"Recruitment or HR",
"Retail",
"Sales",
"Social Work",
"Transport or Logistics",
"Utilities & Telecommunications")
non_standard_industry <- c("Mining and Mentals",
"Aerospace & Defence",
"Automotive",
"Biotechnology",
"Church",
"Food & Bevarage",
"Animal Care",
"Library & Publishing",
"Fashion")
industry_map <- hash()
for(i in standard_industry)
{
industry_map[[as.character(i)]] <- c("")
}
for(i in non_standard_industry)
{
industry_map[[as.character(i)]] <- c("")
}
# ask_a_manager_2021_cleaned%>%
# filter(industry %in% standard_industry)
# roughly 2000 people used the other function within the industry section.....# identify all instances put down to the other category
outliers_table <- ask_a_manager_2021_cleaned%>%
select(industry)%>%
filter(!industry %in% standard_industry)%>%
mutate(industry = tolower(industry))
#make a list out of it
outliers <- tolower(unique(ask_a_manager_2021_cleaned$industry[!ask_a_manager_2021_cleaned$industry %in% standard_industry]))
science_related <- outliers[grep("science",outliers)]3.3.1 Higher education
# Higher education
industry_map[["Education (Higher Education)"]] <- outliers[grep("academ",outliers)]
industry_map[["Education (Higher Education)"]] <- industry_map[["Education (Higher Education)"]][-grep("non-aca",industry_map[["Education (Higher Education)"]])]
industry_map[["Education (Higher Education)"]]<- industry_map[["Education (Higher Education)"]][-grep("not",industry_map[["Education (Higher Education)"]])]
industry_map[["Education (Higher Education)"]]<- c(outliers[grep("higher educ",outliers)],
industry_map[["Education (Higher Education)"]])
industry_map[["Education (Higher Education)"]]<- c(outliers[grep("university",outliers)],
industry_map[["Education (Higher Education)"]])
industry_map[["Education (Higher Education)"]]<- c(outliers[grep("college",outliers)],
industry_map[["Education (Higher Education)"]])
outliers_classified <- industry_map[["Education (Higher Education)"]]3.3.2 Sales
industry_map[["Sales"]] <- c(outliers[grep("procurement",outliers)],
industry_map[["Sales"]])
outliers_classified <-c(industry_map[["Sales"]],
outliers_classified)3.3.3 Banking and Finance
industry_map[["Accounting, Banking & Finance"]] <- c(outliers[grep("acturial",outliers)],
industry_map[["Accounting, Banking & Finance"]])
industry_map[["Accounting, Banking & Finance"]] <- c(outliers[grep("finance",outliers)],
industry_map[["Accounting, Banking & Finance"]])
industry_map[["Accounting, Banking & Finance"]] <- c(outliers[grep("private equity",outliers)],
industry_map[["Accounting, Banking & Finance"]])
outliers_classified <-c(industry_map[["Accounting, Banking & Finance"]],
outliers_classified)3.3.4 Engineering Manufacturing
industry_map[["Engineering or Manufacturing"]] <-c()
industry_map[["Engineering or Manufacturing"]] <- c(outliers[grep("^(?=.*manufactur)(?!.*food)(?!.*aerospace)",outliers, perl = TRUE)],
industry_map[["Engineering or Manufacturing"]])
industry_map[["Engineering or Manufacturing"]] <- c(outliers[grep("mining",outliers, perl = TRUE)],
industry_map[["Engineering or Manufacturing"]])
industry_map[["Engineering or Manufacturing"]] <- c(outliers[grep("oil",outliers, perl = TRUE)],
industry_map[["Engineering or Manufacturing"]])
# industry_map[["Engineering or Manufacturing"]]
outliers_classified <-c(industry_map[["Engineering or Manufacturing"]],
outliers_classified)3.3.5 Health Care
industry_map[["Health care"]] <-c()
industry_map[["Health care"]] <- c(outliers[grep("^(?=.*medical)(?!.*not)",outliers, perl = TRUE)],
industry_map[["Health care"]])
industry_map[["Health care"]] <- c(outliers[grep("pharmaceuticals",outliers)],
industry_map[["Health care"]])
industry_map[["Health care"]] <- c(outliers[grep("beauty",outliers)],
industry_map[["Health care"]])
industry_map[["Health care"]] <- c(outliers[grep("bio",outliers)],
industry_map[["Health care"]])
industry_map[["Health care"]] <- c(outliers[grep("pharma",outliers)],
industry_map[["Health care"]])
industry_map[["Health care"]] <- c(outliers[grep("health",outliers)],
industry_map[["Health care"]])
outliers_classified <-c(industry_map[["Health care"]],
outliers_classified)
# industry_map[["Health care"]]3.3.6 Tech
industry_map[["Computing or Tech"]] <- c(outliers[grep("^tech$",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("^technology",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("software",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("internet",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("video game",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("virtual real",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("ed-tech",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("edtech",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("e-commerce",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("ecommerce",outliers)],
industry_map[["Computing or Tech"]])
industry_map[["Computing or Tech"]] <- c(outliers[grep("technology",outliers)],
industry_map[["Computing or Tech"]])
outliers_classified <-c(industry_map[["Computing or Tech"]],
outliers_classified)3.3.7 Transport or Logistics
industry_map[["Transport or Logistics"]] <- c(outliers[grep("distribution",outliers)],
industry_map[["Transport or Logistics"]])
industry_map[["Transport or Logistics"]] <- c(outliers[grep("import",outliers)],
industry_map[["Transport or Logistics"]])
industry_map[["Transport or Logistics"]] <- c(outliers[grep("supply chain",outliers)],
industry_map[["Transport or Logistics"]])
industry_map[["Transport or Logistics"]] <- c(outliers[grep("wholesale",outliers)],
industry_map[["Transport or Logistics"]])
industry_map[["Transport or Logistics"]] <- c(outliers[grep("warehous",outliers)],
industry_map[["Transport or Logistics"]])
outliers_classified <-c(industry_map[["Transport or Logistics"]],
outliers_classified)3.3.8 Government and Public Administration
industry_map[["Government and Public Administration"]] <- c(outliers[grep("government",outliers)],
industry_map[["Government and Public Administration"]])
industry_map[["Government and Public Administration"]] <- c(outliers[grep("politic",outliers)],
industry_map[["Government and Public Administration"]])
industry_map[["Government and Public Administration"]] <- c(outliers[grep("administration",outliers)],
industry_map[["Government and Public Administration"]])
industry_map[["Government and Public Administration"]] <- c(outliers[grep("public health",outliers)],
industry_map[["Government and Public Administration"]])
industry_map[["Government and Public Administration"]] <- c(outliers[grep("urban planning",outliers)],
industry_map[["Government and Public Administration"]])
industry_map[["Government and Public Administration"]] <- c(outliers[grep("union",outliers)],
industry_map[["Government and Public Administration"]])
industry_map[["Government and Public Administration"]] <- c(outliers[grep("cultur",outliers)],
industry_map[["Government and Public Administration"]])
industry_map[["Government and Public Administration"]] <- c(outliers[grep("park",outliers)],
industry_map[["Government and Public Administration"]])
outliers_classified <-c(industry_map[["Government and Public Administration"]],
outliers_classified)3.3.9 Property or Construction
industry_map[["Property or Construction"]] <- c(outliers[grep("architect",outliers)],
industry_map[["Property or Construction"]])
industry_map[["Property or Construction"]] <- c(outliers[grep("construct",outliers)],
industry_map[["Property or Construction"]])
industry_map[["Property or Construction"]] <- c(outliers[grep("real estate",outliers)],
industry_map[["Property or Construction"]])
industry_map[["Property or Construction"]] <- c(outliers[grep("interior",outliers)],
industry_map[["Property or Construction"]])
industry_map[["Property or Construction"]] <- c(outliers[grep("exterior",outliers)],
industry_map[["Property or Construction"]])
outliers_classified <-c(industry_map[["Property or Construction"]],
outliers_classified)3.3.10 Aerospace & Defence
industry_map[["Aerospace & Defence"]] <- c(outliers[grep("aerospace",outliers)],
industry_map[["Aerospace & Defence"]])
industry_map[["Aerospace & Defence"]] <- c(outliers[grep("defense",outliers)],
industry_map[["Aerospace & Defence"]])
industry_map[["Aerospace & Defence"]] <- c(outliers[grep("defence",outliers)],
industry_map[["Aerospace & Defence"]])
industry_map[["Aerospace & Defence"]] <- c(outliers[grep("military",outliers)],
industry_map[["Aerospace & Defence"]])
industry_map[["Aerospace & Defence"]] <- c(outliers[grep("aviation",outliers)],
industry_map[["Aerospace & Defence"]])
outliers_classified <-c(industry_map[["Aerospace & Defence"]],
outliers_classified)3.3.11 Library Publishing
industry_map[["Library & Publishing"]] <- c(outliers[grep("library",outliers)],
industry_map[["Library & Publishing"]])
industry_map[["Library & Publishing"]] <- c(outliers[grep("libra",outliers)],
industry_map[["Library & Publishing"]])
industry_map[["Library & Publishing"]] <- c(outliers[grep("publishing",outliers)],
industry_map[["Library & Publishing"]])
industry_map[["Library & Publishing"]] <- c(outliers[grep("archiv",outliers)],
industry_map[["Library & Publishing"]])
outliers_classified <-c(industry_map[["Library & Publishing"]],
outliers_classified)3.3.12 Animal Care
industry_map[["Animal Care"]] <- c(outliers[grep("zoo",outliers)],
industry_map[["Animal Care"]])
industry_map[["Animal Care"]] <- c(outliers[grep("veterinary",outliers)],
industry_map[["Animal Care"]])
industry_map[["Animal Care"]] <- c(outliers[grep("veterinarian",outliers)],
industry_map[["Animal Care"]])
industry_map[["Animal Care"]] <- c(outliers[grep("animal",outliers)],
industry_map[["Animal Care"]])
industry_map[["Animal Care"]] <- c(outliers[grep("pet",outliers)],
industry_map[["Animal Care"]])
outliers_classified <-c(industry_map[["Animal Care"]],
outliers_classified)
# industry_map[["Animal Care"]]3.3.13 Food and Bevarage
industry_map[["Food & Bevarage"]] <- c(outliers[grep("wine",outliers)],
industry_map[["Food & Bevarage"]])
industry_map[["Food & Bevarage"]] <- c(outliers[grep("bevarage",outliers)],
industry_map[["Food & Bevarage"]])
industry_map[["Food & Bevarage"]] <- c(outliers[grep("beer",outliers)],
industry_map[["Food & Bevarage"]])
industry_map[["Food & Bevarage"]] <- c(outliers[grep("spirit",outliers)],
industry_map[["Food & Bevarage"]])
industry_map[["Food & Bevarage"]] <- c(outliers[grep("beverage",outliers)],
industry_map[["Food & Bevarage"]])
industry_map[["Food & Bevarage"]] <- c(outliers[grep("food",outliers)],
industry_map[["Food & Bevarage"]])
outliers_classified <-c(industry_map[["Food & Bevarage"]],
outliers_classified)3.3.14 Hospitality
industry_map[["Hospitality & Events"]] <- c(outliers[grep("restaurant",outliers)],
industry_map[["Hospitality & Events"]])
industry_map[["Hospitality & Events"]] <- c(outliers[grep("food service",outliers)],
industry_map[["Hospitality & Events"]])
outliers_classified <-c(industry_map[["Hospitality & Events"]],
outliers_classified)3.3.15 Business or Consulting
industry_map[["Business or Consulting"]] <- c(outliers[grep("consulting",outliers)],
industry_map[["Business or Consulting"]])
industry_map[["Business or Consulting"]] <- c(outliers[grep("environmental",outliers)],
industry_map[["Business or Consulting"]])
industry_map[["Business or Consulting"]] <- c(outliers[grep("consult",outliers)],
industry_map[["Business or Consulting"]])
industry_map[["Business or Consulting"]] <- c(outliers[grep("professional services",outliers)],
industry_map[["Business or Consulting"]])
outliers_classified <-c(industry_map[["Business or Consulting"]],
outliers_classified)3.3.16 Automotive
industry_map[["Automotive"]] <- c(outliers[grep("auto",outliers)],
industry_map[["Automotive"]])
outliers_classified <-c(industry_map[["Automotive"]],
outliers_classified)3.3.17 Human Resources
industry_map[["Recruitment or HR"]] <- c(outliers[grep("workforce",outliers)],
industry_map[["Recruitment or HR"]])
industry_map[["Recruitment or HR"]] <- c(outliers[grep("staffing",outliers)],
industry_map[["Recruitment or HR"]])
industry_map[["Recruitment or HR"]] <- c(outliers[grep("human",outliers)],
industry_map[["Recruitment or HR"]])
outliers_classified <-c(industry_map[["Recruitment or HR"]],
outliers_classified)3.3.18 Non Profit
industry_map[["Nonprofits"]] <- c(outliers[grep("non profit",outliers)],
industry_map[["Nonprofits"]])
industry_map[["Nonprofits"]] <- c(outliers[grep("non-profit",outliers)],
industry_map[["Nonprofits"]])
industry_map[["Nonprofits"]] <- c(outliers[grep("nonprofit",outliers)],
industry_map[["Nonprofits"]])
outliers_classified <-c(industry_map[["Nonprofits"]],
outliers_classified)3.3.19 Overview
values(industry_map)## $`Accounting, Banking & Finance`
## [1] "commercial real estate - private equity"
## [2] "private equity"
## [3] "finance"
## [4] "automotive finance and insurance"
## [5] "finance/investment management but in legal/compliance, so back-office"
## [6] "i work in the finance function of a large global conglomerate"
## [7] "finance"
## [8] "professional association in finance"
## [9] ""
##
## $`Aerospace & Defence`
## [1] "aerospace/aviation"
## [2] "aviation"
## [3] "instructional design, aviation industry"
## [4] "aerospace/aviation"
## [5] "aviation"
## [6] "military"
## [7] "international defence"
## [8] "defense"
## [9] "aerospace/defense"
## [10] "defense contracting"
## [11] "aerospace and defense"
## [12] "defense contracting"
## [13] "research & development (defense industry)"
## [14] "aerospace & defense"
## [15] "aerospace and defense manufacturing"
## [16] "defense contractor"
## [17] "aerospace and defense/government contracting"
## [18] "aerospace/aviation"
## [19] "aerospace contracting"
## [20] "aerospace"
## [21] "aerospace/defense"
## [22] "aerospace data"
## [23] "aerospace and defense"
## [24] "aerospace/aviation"
## [25] "aerospace & defense"
## [26] "aerospace and defense manufacturing"
## [27] "aerospace and defense/government contracting"
## [28] ""
##
## $`Agriculture or Forestry`
## [1] ""
##
## $`Animal Care`
## [1] "pet care industry (dog training/walking)"
## [2] "pet"
## [3] "pet care industry"
## [4] "pet care"
## [5] "petroleum"
## [6] "pet care/grooming"
## [7] "animal health product manufacturing"
## [8] "animal caretaker"
## [9] "animal health industry"
## [10] "animal health"
## [11] "animal welfare"
## [12] "animal care"
## [13] "animal care"
## [14] "veterinarian"
## [15] "veterinary"
## [16] "veterinary medicine"
## [17] "veterinary care"
## [18] "veterinary services"
## [19] "veterinary biotech"
## [20] "veterinary diagnostics"
## [21] "veterinary m&a"
## [22] "veterinary care"
## [23] "zoo"
## [24] "zoos and aquariums"
## [25] ""
##
## $`Art & Design`
## [1] ""
##
## $Automotive
## [1] "automotive" "auto repair"
## [3] "automotive finance and insurance" "automotive repair"
## [5] "automtive repair" "automotive technician"
## [7] "automotive repair" "auto repair"
## [9] "auto mfg." ""
##
## $Biotechnology
## [1] ""
##
## $`Business or Consulting`
## [1] "training and professional services"
## [2] "professional services"
## [3] "professional services / architecture"
## [4] "professional services"
## [5] "consulting / professional services"
## [6] "not-for-profit health research consulting"
## [7] "consulting"
## [8] "environmental consulting"
## [9] "specialist policy consulting/research"
## [10] "not for profit education consultancy"
## [11] "management consulting"
## [12] "political consulting"
## [13] "environmental consulting"
## [14] "lobbying and consulting"
## [15] "architecture and engineering consulting and design"
## [16] "engineering and environmental consulting"
## [17] "consultant"
## [18] "education consulting"
## [19] "political consulting"
## [20] "social science research - not quite academia, not quite nonprofit, not quite consulting"
## [21] "global health consulting"
## [22] "strategy consulting"
## [23] "consulting"
## [24] "consulting / professional services"
## [25] "freelance/self-employed consultant"
## [26] "env. consulting"
## [27] "consulting operations- big 4"
## [28] "environmental consultanting"
## [29] "grantwriting consultants"
## [30] "environmental"
## [31] "environmental science"
## [32] "environmental consulting"
## [33] "environmental regulation"
## [34] "environmental/cultural resource management"
## [35] "environmental sciences"
## [36] "environmental planning"
## [37] "public/environmental health"
## [38] "environmental health + pest control"
## [39] "environmental consulting"
## [40] "engineering and environmental consulting"
## [41] "environmental health and safety compliance"
## [42] "environmental survey"
## [43] "environmental sciences"
## [44] "environmental compliance"
## [45] "environmental compliance/engineering"
## [46] "environmental health and safety"
## [47] "environmental restoration"
## [48] "environmental services"
## [49] "environmental"
## [50] "environmental compliance"
## [51] "environmental consultanting"
## [52] "environmental health and safety"
## [53] "not-for-profit health research consulting"
## [54] "consulting"
## [55] "environmental consulting"
## [56] "specialist policy consulting/research"
## [57] "management consulting"
## [58] "political consulting"
## [59] "environmental consulting"
## [60] "lobbying and consulting"
## [61] "architecture and engineering consulting and design"
## [62] "engineering and environmental consulting"
## [63] "education consulting"
## [64] "political consulting"
## [65] "social science research - not quite academia, not quite nonprofit, not quite consulting"
## [66] "global health consulting"
## [67] "strategy consulting"
## [68] "consulting"
## [69] "consulting / professional services"
## [70] "env. consulting"
## [71] "consulting operations- big 4"
## [72] ""
##
## $Church
## [1] ""
##
## $`Computing or Tech`
## [1] "biotechnology, research and development"
## [2] "biotechnology"
## [3] "educational technology - hybrid between book publishing and technology really"
## [4] "technology/saas"
## [5] "educational technology"
## [6] "information technology"
## [7] "biotechnology/life sciences"
## [8] "technology"
## [9] "marketing technology"
## [10] "medical technology"
## [11] "biotechnology"
## [12] "healthcare information technology"
## [13] "pharmaceuticals/biotechnology"
## [14] "pharma/biotechnology"
## [15] "ecommerce - technology"
## [16] "information technology (it)"
## [17] "healthcare technology"
## [18] "medical technology"
## [19] "pharmaceutical/biotechnology"
## [20] "ecommerce"
## [21] "ecommerce fraud"
## [22] "ecommerce - technology"
## [23] "ecommerce"
## [24] "e-commerce"
## [25] "fashion/e-commerce"
## [26] "e-commerce"
## [27] "edtech"
## [28] "publishing/edtech"
## [29] "education research- mix of edtech and non profits"
## [30] "edtech"
## [31] "virtual reality"
## [32] "video games"
## [33] "video games"
## [34] "video game industry"
## [35] "video games"
## [36] "internet"
## [37] "software development / it"
## [38] "saas company/software"
## [39] "software as a service saas"
## [40] "payroll software"
## [41] "govtech software as a service"
## [42] "software/programming"
## [43] "software"
## [44] "real estate software"
## [45] "software development"
## [46] "software products"
## [47] "biotech/software"
## [48] "technology/saas"
## [49] "technology"
## [50] "tech"
## [51] ""
##
## $`Education (Higher Education)`
## [1] "college athletics"
## [2] "large university administration"
## [3] "library (university)"
## [4] "university administration"
## [5] "university tech transfer (higher ed/marketing/writing)"
## [6] "museum (university affiliated)"
## [7] "fundraising for a university"
## [8] "university research"
## [9] "research at a state university"
## [10] "library at a university"
## [11] "university libraries"
## [12] "hybrid nonprofit higher education (we are part of a university but our entire budget comes from grants)"
## [13] "public health in higher education"
## [14] "fundraising in higher education; nonclinical, nonacademic"
## [15] "data/institutional research in higher education"
## [16] "hybrid nonprofit higher education (we are part of a university but our entire budget comes from grants)"
## [17] "higher education/libraries"
## [18] "higher education fundraising"
## [19] "academic research"
## [20] "academia"
## [21] "research/academia"
## [22] "academic publishing"
## [23] "academic science"
## [24] "academic research (psychology)"
## [25] "academic medicine"
## [26] "academic publishing"
## [27] "academic/nonprofit research"
## [28] "libraries and archives (academic)"
## [29] "academic science"
## [30] "academia--cell and molecular biology"
## [31] "fundraising in higher education; nonclinical, nonacademic"
## [32] "academic scientific research"
## [33] "academic research (social science)"
## [34] "science/research (academia)"
## [35] "science academia"
## [36] "academia / research"
## [37] "publishing (academic)"
## [38] "research - academic"
## [39] "research and development academia"
## [40] "academic research"
## [41] "academic press production"
## [42] "publishing: science, academic, technical"
## [43] "academia - stem"
## [44] "academic publishing"
##
## $`Education (Primary/Secondary)`
## [1] ""
##
## $`Engineering or Manufacturing`
## [1] "oil and gas"
## [2] "trades (supply chain) oil and gas"
## [3] "oil & gas"
## [4] "environment - oil and gas"
## [5] "oilfield adjacent"
## [6] "oil and gas"
## [7] "oil and gas exploration"
## [8] "oil"
## [9] "gas & oil"
## [10] "oil and gas safety training"
## [11] "oil & gas"
## [12] "energy: oil & gas"
## [13] "oil & gas - non destructive testing"
## [14] "energy (oil & gas & associated products, renewable power, etc)"
## [15] "energy - oil and gas"
## [16] "energy, oil & gas"
## [17] "energy/oil"
## [18] "energy sector: oil & gas"
## [19] "energy (oil & gas)"
## [20] "oil & gas"
## [21] "mining"
## [22] "mining and natural resources"
## [23] "mining & mineral processing"
## [24] "construction, mining, manufacturing"
## [25] "engineering - mining"
## [26] "mining/resource extraction"
## [27] "mining/mineral exploration"
## [28] "manufacturing"
## [29] "construction, mining, manufacturing"
## [30] "manufacturing security systems"
## [31] "soft drinks manufacturing"
## [32] "security and manufacturing company"
## [33] "animal health product manufacturing"
## [34] "manufacturing and distributing"
## [35] "apparel manufacture"
## [36] "pharma / medical device design and manufacturing"
## [37] "chemical manufacturing"
## [38] "manufacturing"
## [39] "high end outdoor furniture manufacturer"
## [40] "pharmaceutical manufacturing"
## [41] "synthetic chemical manufacturing"
## [42] "manufacturing (pharmaceuticals)"
## [43] "manufacturing/wholesale"
## [44] "cbd manufacturing"
## [45] "manufacturing : corporate admin support"
## [46] "biotech manufacturing"
## [47] "production and manufacturing"
## [48] "pharmaceutical manufacturing"
## [49] "manufacturing, chemical"
## [50] "r&d in manufacturing"
## [51] "wholesale textile manufacture and sales"
## [52] "manufacturing"
## [53] "pharmaceitical manufacturing"
## [54] "beauty manufacturing & education"
## [55] "manufacturing (medical devices)"
## [56] "manufacturing (personal care)"
##
## $Entertainment
## [1] ""
##
## $Fashion
## [1] ""
##
## $`Food & Bevarage`
## [1] "food processing and packaging"
## [2] "food processing"
## [3] "food distribution"
## [4] "food production"
## [5] "consumer food products"
## [6] "food"
## [7] "food production/processing"
## [8] "food and beverage"
## [9] "food and drink"
## [10] "food manufacturers"
## [11] "food manufacture"
## [12] "food manufacturing"
## [13] "food service"
## [14] "food service"
## [15] "food & beverage"
## [16] "food & beverage production"
## [17] "food and flavor"
## [18] "food demos"
## [19] "restaurant/food service"
## [20] "food & nutrition"
## [21] "biotech/food safety"
## [22] "food manufacturing"
## [23] "food/beverage manufacturing- quality/laboratory"
## [24] "food processing"
## [25] "research and development, food and beverage"
## [26] "food service --- baking"
## [27] "food distribution"
## [28] "food & beverages"
## [29] "warehouse- food and beverage"
## [30] "administration (food service)"
## [31] "food industry"
## [32] "food/quick service restaurant (qsr)"
## [33] "food industry"
## [34] "foodservice"
## [35] "fast food"
## [36] "food and beverage"
## [37] "food & beverage"
## [38] "food & beverage production"
## [39] "beverage production"
## [40] "food/beverage manufacturing- quality/laboratory"
## [41] "research and development, food and beverage"
## [42] "food & beverages"
## [43] "beverage"
## [44] "warehouse- food and beverage"
## [45] "beverage distribution"
## [46] "beverage & spirits"
## [47] "faith/spirituality"
## [48] "beverage & spirits"
## [49] "craft beer industry"
## [50] "beer sales"
## [51] "wine importing/distribution"
## [52] "wine wholesale"
## [53] "wine"
## [54] "winery regulatory compliance"
## [55] ""
##
## $`Government and Public Administration`
## [1] "parks and recreation, land management but with customer service included"
## [2] "parking"
## [3] "horticulture (admin)"
## [4] "cultural (museums/galleries)"
## [5] "culture"
## [6] "arts, culture and heritage"
## [7] "archaeology / cultural resource management"
## [8] "environmental/cultural resource management"
## [9] "cultural resources management/major univ."
## [10] "cultural heritage"
## [11] "agriculture/agriculture chemical"
## [12] "cultural resource management"
## [13] "horticulture"
## [14] "archaeology/cultural resource manager"
## [15] "labor union"
## [16] "wherever i'm assigned via the union"
## [17] "labor union"
## [18] "union/political organizing"
## [19] "unions"
## [20] "urban planning"
## [21] "public health"
## [22] "public health in higher education"
## [23] "public health"
## [24] "public health research"
## [25] "public health"
## [26] "research - public health"
## [27] "public health- state level"
## [28] "public health, local government"
## [29] "public health (not medical)"
## [30] "large university administration"
## [31] "administration, it"
## [32] "university administration"
## [33] "research administration"
## [34] "administration"
## [35] "office administration"
## [36] "administration (food service)"
## [37] "arts administration"
## [38] "pension benefit administration"
## [39] "benefits administration"
## [40] "politics"
## [41] "political campaigning"
## [42] "political campaigns"
## [43] "political consulting"
## [44] "politics/campaigns"
## [45] "politics/campaigns"
## [46] "union/political organizing"
## [47] "political consulting"
## [48] "political campaign"
## [49] "politics/government relations"
## [50] "political research"
## [51] "government research"
## [52] "tourism/heritage -- but for a government building"
## [53] "government relation"
## [54] "government contract"
## [55] "intergovernmental organization"
## [56] "\"government relations\" (lobbying)"
## [57] "government contractor"
## [58] "municipal government (library)"
## [59] "government- scientist"
## [60] "government relations/lobbying"
## [61] "government contracting"
## [62] "federal government contracting"
## [63] "government contracting"
## [64] "government relations"
## [65] "government contractor"
## [66] "government affairs/lobbying"
## [67] "public library (might be considered government, but that always seems an odd designation...)"
## [68] "government contracting (data analytics and program evaluations)"
## [69] "science research, government"
## [70] "public library (non-profit, but also government?)"
## [71] "government contractor (r&d)"
## [72] "government relations"
## [73] "science/government"
## [74] "government contractor, international development"
## [75] "aerospace and defense/government contracting"
## [76] "public health, local government"
## [77] "politics/government relations"
## [78] "government"
## [79] "government contracting r&d"
## [80] ""
##
## $`Health care`
## [1] "not-for-profit health research consulting"
## [2] "research (health)"
## [3] "public health"
## [4] "animal health product manufacturing"
## [5] "public health in higher education"
## [6] "health insurance"
## [7] "healthcare it"
## [8] "public health"
## [9] "public health research"
## [10] "public/environmental health"
## [11] "environment, health, and safety"
## [12] "environmental health + pest control"
## [13] "public health"
## [14] "behavioral health"
## [15] "health research"
## [16] "research - public health"
## [17] "mental health"
## [18] "mental health"
## [19] "mental health therapist"
## [20] "environmental health and safety compliance"
## [21] "health and fitness"
## [22] "healthcare information technology"
## [23] "animal health industry"
## [24] "global health consulting"
## [25] "animal health"
## [26] "non-profit health care (i couldn’t select both)"
## [27] "public health- state level"
## [28] "mental health"
## [29] "behavior analysis/mental health"
## [30] "healthcare technology"
## [31] "public health, local government"
## [32] "environmental health and safety"
## [33] "public health (not medical)"
## [34] "environmental health and safety"
## [35] "pharmaceutical"
## [36] "pharmaceutical research"
## [37] "pharmaceuticals"
## [38] "biotech pharmaceuticals"
## [39] "pharma"
## [40] "pharmaceutical research & development"
## [41] "pharma"
## [42] "biopharma"
## [43] "pharma / medical device design and manufacturing"
## [44] "pharmaceutical"
## [45] "pharma/biotech"
## [46] "pharmaceutical manufacturing"
## [47] "biotech/pharma"
## [48] "biotech/pharma"
## [49] "pharmaceutical r&d"
## [50] "manufacturing (pharmaceuticals)"
## [51] "pharmaceuticals r&d"
## [52] "pharmaceutical development"
## [53] "retail pharmacy"
## [54] "biotech/pharmaceuticals"
## [55] "pharmacuticals"
## [56] "biotech/pharmaceuticals"
## [57] "biotech / pharmaceutical industry"
## [58] "real world evidence (data for pharma research)"
## [59] "big pharma"
## [60] "pharmaceutical manufacturing"
## [61] "pharmaceutical research (chemist)"
## [62] "pharma/biotech"
## [63] "pharmaceutical/contract research organization"
## [64] "pharmaceutical/biotech"
## [65] "pharmaceutical research"
## [66] "pharmaceuticals/biotechnology"
## [67] "pharma/biotechnology"
## [68] "pharmaceutical industry"
## [69] "medical/pharmaceutical"
## [70] "pharmaceuticals / biotech"
## [71] "language services company, unsure the broad category to use. our clients are branding agencies, and their clients are frequently (but not always) pharmaceutical companies."
## [72] "pharmaceuticals"
## [73] "pharmaceutical/biotechnology"
## [74] "pharmaceutical industry"
## [75] "pharma & biotech"
## [76] "biopharmaceuticals"
## [77] "pharmaceitical manufacturing"
## [78] "research scientist, pharma"
## [79] "pharma research"
## [80] "pharmaceutical company"
## [81] "pharma r&d"
## [82] "pharma/ research"
## [83] "science/biotech"
## [84] "biotechnology, research and development"
## [85] "biotechnology"
## [86] "biotech pharmaceuticals"
## [87] "biotech industry"
## [88] "biotech (r&d)"
## [89] "biotech"
## [90] "probiotics"
## [91] "biological research"
## [92] "biologist"
## [93] "biopharma"
## [94] "pharma/biotech"
## [95] "biotech"
## [96] "biotech/pharma"
## [97] "biotech/pharma"
## [98] "biomedical research"
## [99] "academia--cell and molecular biology"
## [100] "biotech / research"
## [101] "bio tech"
## [102] "biotechnology/life sciences"
## [103] "biotech / life sciences"
## [104] "biology/research"
## [105] "biotech/pharmaceuticals"
## [106] "biotech manufacturing"
## [107] "bioscience company"
## [108] "biotech research"
## [109] "science (research, biology)"
## [110] "biomedical research"
## [111] "biotech/pharmaceuticals"
## [112] "biomedical research"
## [113] "biotech / pharmaceutical industry"
## [114] "biotech/food safety"
## [115] "biological sciences"
## [116] "biotech research"
## [117] "biotechnology"
## [118] "veterinary biotech"
## [119] "pharma/biotech"
## [120] "pharmaceutical/biotech"
## [121] "biotech"
## [122] "lab science (biotech)"
## [123] "pharmaceuticals/biotechnology"
## [124] "pharma/biotechnology"
## [125] "biotech/drug development"
## [126] "pharmaceuticals / biotech"
## [127] "pharmaceutical/biotechnology"
## [128] "pharma & biotech"
## [129] "biopharmaceuticals"
## [130] "biotech/software"
## [131] "bioinformatics"
## [132] "beauty"
## [133] "beauty/service industry"
## [134] "beauty /cpg"
## [135] "beauty, cosmetics, fragrance"
## [136] "beauty manufacturing & education"
## [137] "pharmaceuticals"
## [138] "biotech pharmaceuticals"
## [139] "manufacturing (pharmaceuticals)"
## [140] "pharmaceuticals r&d"
## [141] "biotech/pharmaceuticals"
## [142] "biotech/pharmaceuticals"
## [143] "pharmaceuticals/biotechnology"
## [144] "pharmaceuticals / biotech"
## [145] "pharmaceuticals"
## [146] "biopharmaceuticals"
## [147] "medical research"
## [148] "medical research"
## [149] "third sector/non profit - medical membership in uk"
## [150] "medical sciences"
## [151] "medical devices"
## [152] "pharma / medical device design and manufacturing"
## [153] "medical library"
## [154] "libraries (medical)"
## [155] "biomedical research"
## [156] "medical communications"
## [157] "medical communications"
## [158] "biomedical research"
## [159] "biomedical research"
## [160] "medical technology"
## [161] "stem medical research"
## [162] "medical device"
## [163] "medical research"
## [164] "diagnostic medical devices"
## [165] "medical/pharmaceutical"
## [166] "medical supply wholesale & warehousing"
## [167] "medical technology"
## [168] "medical interpreter -(spanish)"
## [169] "manufacturing (medical devices)"
##
## $`Hospitality & Events`
## [1] "food service" "food service"
## [3] "restaurant/food service" "food service --- baking"
## [5] "administration (food service)" "restaurant"
## [7] "restaurant/food service" "restaurant group"
## [9] "restaurant/service" "fast casual restaurant"
## [11] "food/quick service restaurant (qsr)" "restaurants & hospitality"
## [13] ""
##
## $Insurance
## [1] ""
##
## $Law
## [1] ""
##
## $`Law Enforcement & Security`
## [1] ""
##
## $`Leisure, Sport & Tourism`
## [1] ""
##
## $`Library & Publishing`
## [1] "libraries and archives"
## [2] "information management/archives"
## [3] "archives"
## [4] "libraries & archives"
## [5] "libraries and archives (academic)"
## [6] "library/archives"
## [7] "library/archive"
## [8] "library/archive/research center"
## [9] "archives/library science"
## [10] "museums & archives"
## [11] "libraries / archives / information"
## [12] "archives/libraries"
## [13] "libraries/museums/archives"
## [14] "libraries/archives"
## [15] "museums & archives (not sure where this would fall)"
## [16] "publishing"
## [17] "educational publishing"
## [18] "publishing (book)"
## [19] "scientific publishing"
## [20] "academic publishing"
## [21] "educational technology - hybrid between book publishing and technology really"
## [22] "publishing"
## [23] "academic publishing"
## [24] "editor in educational publishing"
## [25] "education publishing"
## [26] "educational publishing"
## [27] "book publishing"
## [28] "book publishing"
## [29] "publishing/edtech"
## [30] "digital marketing within a book publishing company (please reclassify as you see fit)"
## [31] "customer service/publishing-adjacent"
## [32] "publishing (academic)"
## [33] "educational publishing / ed tech"
## [34] "science publishing"
## [35] "publishing: science, academic, technical"
## [36] "academic publishing"
## [37] "tabletop games publishing"
## [38] "publishing, content as a service"
## [39] "print publishing"
## [40] "libraries and archives"
## [41] "library"
## [42] "libraries"
## [43] "public libraries"
## [44] "library"
## [45] "public library"
## [46] "library tech for a school system"
## [47] "law library"
## [48] "public library"
## [49] "library (university)"
## [50] "library/information managment"
## [51] "museum library"
## [52] "public librarian"
## [53] "public library"
## [54] "public library (technically city govt.?)"
## [55] "librarian"
## [56] "municipal library"
## [57] "medical library"
## [58] "libraries & archives"
## [59] "library (its a non-profit and its a govt job - how would i list that? not all libraries are govt jobs)"
## [60] "libraries and archives (academic)"
## [61] "municipal government (library)"
## [62] "library/archives"
## [63] "special collections library"
## [64] "libraries (medical)"
## [65] "librarian--contractor for nasa"
## [66] "library page (public county library)"
## [67] "information services (libraries)"
## [68] "public libraries"
## [69] "public libraries"
## [70] "library/archive"
## [71] "librarian and assistant manager of a library"
## [72] "librarian in legal setting"
## [73] "municipal (public) libraries"
## [74] "professional public librarian"
## [75] "public library (might be considered government, but that always seems an odd designation...)"
## [76] "library/archive/research center"
## [77] "archives/library science"
## [78] "public library (non-profit, but also government?)"
## [79] "library--public"
## [80] "information services (library)"
## [81] "libraries (public)"
## [82] "libraries / archives / information"
## [83] "libraries (public)"
## [84] "libraries"
## [85] "library science / part-time work/study"
## [86] "archives/libraries"
## [87] "library and information services"
## [88] "library and information science"
## [89] "libraries/museums/archives"
## [90] "library at a university"
## [91] "university libraries"
## [92] "libraries/archives"
## [93] "libraries"
## [94] "higher education/libraries"
## [95] "public library"
## [96] "information services/libraries"
## [97] "public/research library"
## [98] "library"
## [99] "library"
## [100] "public library"
## [101] "library tech for a school system"
## [102] "law library"
## [103] "public library"
## [104] "library (university)"
## [105] "library/information managment"
## [106] "museum library"
## [107] "public library"
## [108] "public library (technically city govt.?)"
## [109] "municipal library"
## [110] "medical library"
## [111] "library (its a non-profit and its a govt job - how would i list that? not all libraries are govt jobs)"
## [112] "municipal government (library)"
## [113] "library/archives"
## [114] "special collections library"
## [115] "library page (public county library)"
## [116] "library/archive"
## [117] "librarian and assistant manager of a library"
## [118] "public library (might be considered government, but that always seems an odd designation...)"
## [119] "library/archive/research center"
## [120] "archives/library science"
## [121] "public library (non-profit, but also government?)"
## [122] "library--public"
## [123] "information services (library)"
## [124] "library science / part-time work/study"
## [125] "library and information services"
## [126] "library and information science"
## [127] "library at a university"
## [128] "public library"
## [129] "public/research library"
## [130] ""
##
## $`Marketing, Advertising & PR`
## [1] ""
##
## $`Media & Digital`
## [1] ""
##
## $`Mining and Mentals`
## [1] ""
##
## $Nonprofits
## [1] "computing/tech + higher ed + nonprofit"
## [2] "museum - nonprofit"
## [3] "academic/nonprofit research"
## [4] "museums: nonprofit"
## [5] "nonprofit scholarly society publisher"
## [6] "social science research - not quite academia, not quite nonprofit, not quite consulting"
## [7] "nonprofit - legal department"
## [8] "nonprofit - lort d theater"
## [9] "nonprofit association"
## [10] "affordable housing real estate development (nonprofit)"
## [11] "hybrid nonprofit higher education (we are part of a university but our entire budget comes from grants)"
## [12] "library (its a non-profit and its a govt job - how would i list that? not all libraries are govt jobs)"
## [13] "public library (non-profit, but also government?)"
## [14] "non-profit health care (i couldn’t select both)"
## [15] "non-profit theatre"
## [16] "third sector/non profit - medical membership in uk"
## [17] "marketing at a non profit"
## [18] "non profit theater"
## [19] "non profit theater"
## [20] "education research- mix of edtech and non profits"
## [21] ""
##
## $`Property or Construction`
## [1] "interior design & architecture"
## [2] "interior design (commercial)"
## [3] "interior landscaping"
## [4] "real estate development"
## [5] "commercial real estate tenancy"
## [6] "real estate"
## [7] "real estate"
## [8] "commercial real estate"
## [9] "real estate services"
## [10] "real estate corp. office/not a realtor"
## [11] "real estate investment"
## [12] "title/real estate"
## [13] "real estate"
## [14] "real estate: title & escrow"
## [15] "commercial real estate - private equity"
## [16] "real estate/ mortgage"
## [17] "real estate investment support"
## [18] "real estate software"
## [19] "real estate/development"
## [20] "retail real estate"
## [21] "real estate affordable housing"
## [22] "affordable housing real estate development (nonprofit)"
## [23] "real estate servicea"
## [24] "real estate customer care"
## [25] "real estate valuation"
## [26] "real estate title company"
## [27] "real estate association"
## [28] "commercial real estate data and analytics/research"
## [29] "construction"
## [30] "construction"
## [31] "construction, mining, manufacturing"
## [32] "construction / stone industry"
## [33] "construction, hvac"
## [34] "architecture/construction"
## [35] "construction management"
## [36] "architecture & construction"
## [37] "architecture, engineering, construction"
## [38] "architecture"
## [39] "interior design & architecture"
## [40] "architecture"
## [41] "architect"
## [42] "architecture and engineering consulting and design"
## [43] "architecture / engineering"
## [44] "architectural/land planning/civil engineering"
## [45] "architecture/construction"
## [46] "professional services / architecture"
## [47] "architecture & construction"
## [48] "architecture, engineering, construction"
## [49] "landscape architecture"
## [50] ""
##
## $`Recruitment or HR`
## [1] "human resources" "human capital management"
## [3] "human services" "staffing industrry"
## [5] "staffing agency" "staffing firm"
## [7] "staffing & workforce solutions" "workforce development"
## [9] "staffing & workforce solutions" ""
##
## $Retail
## [1] ""
##
## $Sales
## [1] "procurement" "procurement/sourcing/operations"
## [3] "sourcing & procurement" ""
##
## $`Social Work`
## [1] ""
##
## $`Transport or Logistics`
## [1] "warehousing"
## [2] "warehouse- food and beverage"
## [3] "medical supply wholesale & warehousing"
## [4] "warehouse"
## [5] "wholesale distribution b2b"
## [6] "wholesale and retail trade"
## [7] "wholesale distribution"
## [8] "wholesale - apparel"
## [9] "manufacturing/wholesale"
## [10] "wholesale trade"
## [11] "wholesale supplier"
## [12] "wholesale/distrbution"
## [13] "wine wholesale"
## [14] "children's book wholesale"
## [15] "wholesale"
## [16] "wholesale textile manufacture and sales"
## [17] "medical supply wholesale & warehousing"
## [18] "wholesale industrial & welding supplies & equipment"
## [19] "trades (supply chain) oil and gas"
## [20] "supply chain"
## [21] "supply chain distribution"
## [22] "supply chain"
## [23] "supply chain!"
## [24] "wine importing/distribution"
## [25] "coffee - importing"
## [26] "wholesale distribution b2b"
## [27] "food distribution"
## [28] "wine importing/distribution"
## [29] "wholesale distribution"
## [30] "supply chain distribution"
## [31] "distribution"
## [32] "grocery distribution"
## [33] "commercial building material distribution"
## [34] "food distribution"
## [35] "beverage distribution"
## [36] ""
##
## $`Utilities & Telecommunications`
## [1] ""# check outliers reamining
outliers_left <- outliers_table%>%
filter(!industry %in% outliers_classified)%>%
group_by(industry)%>%
summarise(total = n())
# outliers_leftThe remaining data is too fragmented to be worth further cleaning.
test <- ask_a_manager_2021_cleaned%>%
mutate(industry_cleaned = case_when(industry %in% standard_industry ~ industry))%>%
mutate(industry_lower = tolower(industry))
for(i in standard_industry)
{
test<-test%>%
mutate(industry_cleaned = case_when(industry_lower %in% values(industry_map, keys = i) ~ i,
TRUE ~ industry_cleaned))
}
ask_a_manager_2021_cleaned <- testAdding ISCO classification to the dataset
occupation_classification <- labourR::classify_occupation(corpus = ask_a_manager_2021_cleaned,
isco_level = 2,
lang = "en",
num_leaves = 10,
text_col = "industry",
id = "timestamp")%>%
mutate(timestamp = ymd_hms(timestamp))
ask_a_manager_2021_cleaned <- ask_a_manager_2021_cleaned%>%
mutate(timestamp = ymd_hms(timestamp))
ask_a_manager_2021_cleaned <- left_join(ask_a_manager_2021_cleaned,
occupation_classification,
by = "timestamp")%>%
clean_names()
labR_classifications <- unique(occupation_classification$preferredLabel)3.4 Clean Gender
ask_a_manager_2021_cleaned <- ask_a_manager_2021_cleaned%>%
mutate(gender = case_when(gender == "Prefer not to answer" ~ "Other or prefer not to answer",
TRUE ~ gender))3.5 Save cleaned Data
write_csv(ask_a_manager_2021_cleaned, here::here("data","ask_manager_2021_cleaned.csv"))#optionally load cleaned dataset from here
ask_a_manager_2021_cleaned <- read_csv(here::here("data","ask_manager_2021_cleaned.csv"))4 Dataset Overview Statistics
4.1 Number of responses with each number of Races
n_race_responses <- ask_a_manager_2021_cleaned%>%
mutate(race = str_split(race,pattern = ","))%>%
rowwise()%>%
mutate(n_race = length(race))
n_race_sumary <- n_race_responses%>%
group_by(n_race)%>%
summarise(n = n())%>%
mutate(prct = n/sum(n))
n_race_responses <- left_join(n_race_responses, n_race_sumary, by = "n_race")
ggplot(n_race_responses,
aes(n_race))+
geom_bar(alpha = 0.8)+
scale_y_log10()+
theme_bw()+
labs(title = "Number of Races survey respondents Identified with",
x = "Number of Races",
y = "Count (y axis scaled to log10)")+
scale_x_continuous(breaks = pretty_breaks(6))+
geom_text(aes(label = formatC(..count.., format = "f", big.mark = ",", digits = 0)),
stat = "count",
vjust = -0.4)
n_race_sumary <- n_race_sumary %>%
mutate(prct = round(prct*100, 2),
n = formatC(n, format = "f", big.mark = ",", digits = 0))
colnames(n_race_sumary) <- c("Number of Races Respondant Identifies with",
"Number of Respondants",
"% of Of total respodants")
n_race_sumary| Number of Races Respondant Identifies with | Number of Respondants | % of Of total respodants |
|---|---|---|
| 1 | 25,058 | 93.6 |
| 2 | 691 | 2.58 |
| 3 | 560 | 2.09 |
| 4 | 415 | 1.55 |
| 5 | 37 | 0.14 |
| 6 | 4 | 0.01 |
93.62% of the respondents selected a single race when filling out the survey.
### clean the data for race statistics with single race responses.
single_race_data <- n_race_responses%>%
mutate(multiple_race = case_when(n_race > 1 ~1,
n_race == 1 ~0))%>%
filter(multiple_race == 0)%>% # only use responses that only put down one race
mutate(race = as.character(race[1]))%>%
filter(!is.na(race))
single_race_data <-single_race_data%>%
group_by(country)%>%
mutate(num_resp_country = n())%>%
ungroup()%>%
group_by(race)%>%
mutate(num_resp_race = n())%>%
ungroup()4.2 Race Distribtion of the Dataset
single_race_data%>%
filter(!is.na(race))%>%
group_by(race)%>%
summarise(num_resp_race = n())%>%
ggplot(aes(x = num_resp_race, y = reorder(race, num_resp_race)))+
geom_col(fill = "skyblue")+
theme_bw()+
labs(title = "Number of Responses By Gender",
x = "number of responses")+
theme(axis.title.y = element_blank())+
geom_text(aes(label = sprintf("%.2f%%", num_resp_race/sum(num_resp_race)*100)), hjust = 0.5)
4.3 Number of Responses by Gender
ask_a_manager_2021_cleaned%>%
filter(!is.na(gender))%>%
group_by(gender)%>%
summarise(num_resp_gender = n())%>%
ggplot(aes(x = num_resp_gender, y = reorder(gender, num_resp_gender)))+
geom_col(fill = "skyblue")+
theme_bw()+
labs(title = "Number of Responses By Gender",
x = "number of responses")+
theme(axis.title.y = element_blank())+
geom_text(aes(label = sprintf("%.2f%%", num_resp_gender/sum(num_resp_gender)*100)), hjust = 0.5)
4.4 Number of Responses by Country
number_resposes_country <- ask_a_manager_2021_cleaned%>%
filter(!is.na(country))%>%
group_by(country)%>%
summarise(num_resp_country = n())%>%
arrange(desc(num_resp_country))
number_resposes_country| country | num_resp_country |
|---|---|
| United States | 22160 |
| Canada | 1607 |
| United Kingdom | 1546 |
| Australia | 377 |
| Germany | 182 |
| Ireland | 120 |
| New Zealand | 117 |
| Netherlands | 80 |
| France | 62 |
| Spain | 45 |
| Sweden | 39 |
| Switzerland | 37 |
| Belgium | 32 |
| Japan | 29 |
| Denmark | 22 |
| South Africa | 17 |
| Finland | 16 |
| Austria | 15 |
| India | 14 |
| Israel | 14 |
| Italy | 14 |
| Malaysia | 13 |
| Norway | 13 |
| Singapore | 13 |
| Brazil | 12 |
| Philippines | 9 |
| China | 8 |
| Mexico | 8 |
| Argentina | 7 |
| Czechia | 6 |
| Poland | 6 |
| Greece | 5 |
| Portugal | 5 |
| South Korea | 5 |
| Hong Kong SAR China | 4 |
| Puerto Rico | 4 |
| Romania | 4 |
| Thailand | 4 |
| Latvia | 3 |
| Luxembourg | 3 |
| Pakistan | 3 |
| Taiwan | 3 |
| Bangladesh | 2 |
| Bermuda | 2 |
| Bulgaria | 2 |
| Estonia | 2 |
| Ghana | 2 |
| Hungary | 2 |
| Indonesia | 2 |
| Kenya | 2 |
| Lithuania | 2 |
| Sri Lanka | 2 |
| United Arab Emirates | 2 |
| Vietnam | 2 |
| Afghanistan | 1 |
| Bahamas | 1 |
| Cambodia | 1 |
| Cayman Islands | 1 |
| Chile | 1 |
| Colombia | 1 |
| Congo - Brazzaville | 1 |
| Costa Rica | 1 |
| Côte d’Ivoire | 1 |
| Croatia | 1 |
| Cuba | 1 |
| Ecuador | 1 |
| Eritrea | 1 |
| Isle of Man | 1 |
| Jamaica | 1 |
| Jordan | 1 |
| Kuwait | 1 |
| Malta | 1 |
| Morocco | 1 |
| Nigeria | 1 |
| Panama | 1 |
| Qatar | 1 |
| Russia | 1 |
| Rwanda | 1 |
| Saudi Arabia | 1 |
| Serbia | 1 |
| Sierra Leone | 1 |
| Slovakia | 1 |
| Slovenia | 1 |
| Somalia | 1 |
| Trinidad & Tobago | 1 |
| Turkey | 1 |
| Uganda | 1 |
| Ukraine | 1 |
| Uruguay | 1 |
| Zimbabwe | 1 |
ask_a_manager_2021_cleaned%>%
filter(!is.na(country))%>%
group_by(country)%>%
summarise(num_resp_country = n())%>%
filter(num_resp_country > 50)%>%
ggplot(aes(x = num_resp_country, y = reorder(country, num_resp_country)))+
geom_col(fill = "skyblue")+
theme_bw()+
labs(title = "Number of Responses By Country (with more than 50 responses)",
x = "number of responses")+
theme(axis.title.y = element_blank())+
geom_text(aes(label = sprintf("%.2f%%", num_resp_country/sum(num_resp_country)*100)), hjust = 0.5)
Number of Resp/Country
We can see that Data from the United States dominates the dataset.
4.5 Race distribution from countries with more then 50 responses
country_race <- single_race_data%>%
filter(num_resp_country > 50, !is.na(race))%>%
rowwise()%>%
mutate(race = as.character(race[1]))%>%
group_by(country, race)%>%
summarise(num_resp_country, race_count = n())%>%
mutate(race_prc = race_count/num_resp_country)%>%
summarise(num_resp_country = max(num_resp_country),
race_count = max(race_count),
race_prc = max(race_prc))%>%
mutate(race_prc = round(race_prc*100,2))
ggplot(country_race,aes(y = reorder(race, race_prc), x = race_prc))+
geom_col( aes(fill = race))+
facet_wrap(~country)+
theme(axis.title.y = element_blank(),
plot.title = element_text(),
axis.text.y = element_blank(),
legend.position = "bottom")+
labs(title = "Population Distribution in Countries with more then 50 respondants",
x = "% of Respondents",
fill = "Race Classification")
The race distribution of the dataset is not representative of the countries population. For example the United States has an African American population of over 13% while in the dataset it below 5%.
4.6 Gender distribution from countries with more then 50 responses
ask_a_manager_2021_cleaned%>%
group_by(country)%>%
mutate(num_resp_country = n())%>%
ungroup()%>%
filter(!is.na(gender), num_resp_country>50)%>%
group_by(country,gender)%>%
summarise(num_resp_gender = n())%>%
mutate(prc = num_resp_gender/sum(num_resp_gender)*100)%>%
ggplot(aes(x = prc, y = reorder(gender, prc)))+
geom_col(fill = "skyblue")+
theme_bw()+
labs(title = "% of Responses By Gender",
x = "%")+
theme(axis.title.y = element_blank())+
geom_text(aes(label = sprintf("%.2f%%", prc)), hjust = 0.5, size =2)+
facet_wrap(~country)
From the top countries the responses overwhelmingly are from women, execpt the netherlands where the data is very close to balanced.
4.7 Race by Industry
industry_race <- single_race_data%>%
filter(!is.na(industry_cleaned), num_resp_country > 50, !is.na(country), !is.na(race))%>%
group_by(industry_cleaned, race)%>%
summarise(race_count = n())%>%
mutate(race_prc = race_count/sum(race_count))%>%
mutate(race_prc = round(race_prc*100,2))%>%
ungroup()%>%
group_by(industry_cleaned)%>%
mutate(industry_total = sum(race_count))
ggplot(industry_race, aes(y = reorder(industry_cleaned, race_count), x = race_count, fill = race ))+
geom_col()+
labs(title = "Race distribution within Industries",
x = "Number of Respondants",
y = "Industry Classification")+
theme_bw()+
theme(legend.position = "bottom",
axis.title.y = element_blank())
As the Race is not representative of the countries real population this graph only informs us of the distribution of the survey respondents across industries and it is more revealing of the blogs reader base rather then industry statistics.
4.8 By Gender Statistics
#Table giving counts for each gender category dropping those who left the question blank
ask_a_manager_2021_cleaned %>%
drop_na(gender) %>%
count(gender, sort=T, .drop=T) | gender | n |
|---|---|
| Woman | 20731 |
| Man | 4880 |
| Non-binary | 723 |
| Other or prefer not to answer | 276 |
#Summary Statistics of Gender and Salary in USD
ask_a_manager_2021_cleaned %>%
filter(salary_usd != 0)%>%
group_by(gender) %>%
drop_na(gender) %>%
summarise(MeanSalary=mean(salary_usd, na.rm=TRUE),
MedianSalary=median(salary_usd, na.rm=T),
sdsalary= sd(salary_usd, na.rm=TRUE),
q1salary=quantile(salary_usd, prob=.25, na.rm=TRUE),
q3salary=quantile(salary_usd, prob=.75, na.rm=TRUE),
minsalary=min(salary_usd, na.rm=T),
maxsalary=max(salary_usd, na.rm=T)) | gender | MeanSalary | MedianSalary | sdsalary | q1salary | q3salary | minsalary | maxsalary |
|---|---|---|---|---|---|---|---|
| Man | 1.1e+05 | 9.5e+04 | 7.86e+04 | 6.5e+04 | 1.4e+05 | 1e+03 | 1.65e+06 |
| Non-binary | 7.85e+04 | 5.79e+04 | 1.48e+05 | 4.16e+04 | 8.4e+04 | 3.01e+03 | 3.6e+06 |
| Other or prefer not to answer | 9.01e+04 | 7.67e+04 | 7.08e+04 | 5.07e+04 | 1.1e+05 | 7.18e+03 | 8e+05 |
| Woman | 8.37e+04 | 7.2e+04 | 6.63e+04 | 5.3e+04 | 1e+05 | 1e+03 | 5e+06 |
ask_a_manager_2021_cleaned_gender <- ask_a_manager_2021_cleaned %>%
drop_na(gender)
write.csv(ask_a_manager_2021_cleaned_gender, "test.csv")5 Relationships in the Data
5.1 Multiple Race by Gender and Age
Distribution of men and women who identify as more than one Race
m_race_gender_summary <- n_race_responses%>%
mutate(gender = factor(gender,
levels = c("Man","Woman","Non-binary","Other or prefer not to answer", "NA")))%>%
filter(n_race > 1, !is.na(gender))%>%
group_by(gender, n_race, how_old_are_you)%>%
summarise(number = n())%>%
mutate(prct = number/sum(number))
ggplot(m_race_gender_summary, aes(y = number, x = reorder(n_race, number)))+
geom_col()+
facet_grid(gender~how_old_are_you)+
coord_flip()+
theme_bw()+
labs(
title = "Number of Race's a person identifies with by Age and Gender",
x = "Number of Race options ticked by Respondant",
y = "Number of Respondants"
)
If we analyse the respondents that selected more then 1 race that they identify with we can see from the graph that the most prominent age groups are 25-34 and and 35-44 which would mean that Millenials are the most likely identify with multiple race’s. And within the Millennial group it seems like women are more likely to do that then men. This shall be evaluated by a hypothesis testing.
H0 = Millennial women are equally likely to identify with multiple races as millennial men. H1 = Millennial women are not equally likely to identify with multiple races as millennial men.
n_race_responses <- n_race_responses%>%
mutate(multiple_race = case_when(n_race > 1 ~ 1,
n_race == 1 ~0))
mill_men <- subset(n_race_responses,
(n_race_responses$how_old_are_you == "25-34" || n_race_responses$how_old_are_you == "35-44") & n_race_responses$gender == "Man",
select = c(multiple_race))
mill_menmill_women <- subset(n_race_responses,
(n_race_responses$how_old_are_you == "25-34" || n_race_responses$how_old_are_you == "35-44") & n_race_responses$gender == "Woman",
select = c(multiple_race))
mill_woment.test(mill_men, mill_women)##
## Welch Two Sample t-test
##
## data: mill_men and mill_women
## t = 2, df = 7114, p-value = 0.09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.00116 0.01448
## sample estimates:
## mean of x mean of y
## 0.0686 0.0620H0 cannot as p-value is above 0.05. Which means there is no difference in the likelihood of identifying with multiple ethnic bakcgrounds between millenial men and women.
Now lets test for the chance whether the being a millennial truly increases the likelihood of identifying with multiple races.
H0 = The proportion of millennials identifying with multiple races is the same as the proportion of other age groups that identify with multiple races.
H1 = The proportion of millennials identifying with multiple races is not the same as the proportion of other age groups that identify with multiple races.
millennial <- subset(n_race_responses,
(n_race_responses$how_old_are_you == "25-34" || n_race_responses$how_old_are_you == "35-44"),
select = c(multiple_race))
non_millenial <- subset(n_race_responses,
(n_race_responses$how_old_are_you != "25-34" & n_race_responses$how_old_are_you != "35-44"),
select = c(multiple_race))
t.test(millennial, non_millenial)##
## Welch Two Sample t-test
##
## data: millennial and non_millenial
## t = 4, df = 7883, p-value = 2e-04
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.00591 0.01925
## sample estimates:
## mean of x mean of y
## 0.0638 0.0512The H0 hypothesis can be rejected as t value is above 2. Which means that it can be said with 95% confidence that there is a difference between the proportion of millennials that identify with multiple races and all other age groups.
5.2 What is the most popular industry among the top five countries with the most responses?
We would like to see the popularity of the Computing or Tech industry in each country:
library(infer)
#we want to work on the top 5 countries with the most data points..
top5_country <- c("USA", "GBR","CAN", "AUS" , "DEU")
#Industry exploration: gives salary by the selected countries and all industry
salary_exploration <- ask_a_manager_2021_cleaned %>%
filter(!is.na(salary_usd), !is.na(industry_cleaned), !is.na(gender)) %>%
filter(country_code %in% top5_country) %>%
select(country, country_code, salary_usd, industry_cleaned, gender) %>%
group_by(country,country_code, industry_cleaned, gender)%>%
summarise(num_resp = n())%>%
group_by(country)%>%
mutate(prc = num_resp/sum(num_resp)*100)
# group_by(country, industry_cleaned)%>%
# mutate(num_resp)
# salary_exploration
#Choosing the most popular industries in the survey for the designated countries
industry_count <- salary_exploration %>%
group_by(industry_cleaned) %>%
count() %>%
arrange(desc(n))
industry_countggplot(salary_exploration, aes(x = prc, y = reorder(industry_cleaned, prc)))+
geom_col(aes(fill = gender))+
facet_wrap(~country)+
theme_bw()+
labs(title = "% of Respondants in Each Industry by Country",
x = "%")+
theme(axis.title.y = element_blank(),
legend.position = "bottom")
% of Industry and Gender distribution
Overall Computing or Tech is the most popular sector among the 5 chosen countries, followed by Education, Nonprofits…
What we can also see from the graph that most of the blogs male readers are from within the tech industry.
In the US, the most popular industry is Computing or Tech like the overall population. Since US has the most datapoints, it might have affected the distribution of the popularity of tech overall in global data, but this does not seem to be the case.
In every single country on our country list, tech is the most popular industry. Now, we would like to see the mean and median tech salary by each country. That’s why, it is reasonable to focus on the Tech sector.
Boxplot of Computing or Tech salaries across USA, Australia, Germany, UK, Canada
tech_salary_boxplot_data <- ask_a_manager_2021_cleaned %>%
filter(industry_cleaned =="Computing or Tech")%>%
filter(country_code %in% top5_country)%>%
group_by(country)%>%
mutate(max_salary = max(salary_usd))
tech_salary_boxplot <- ggplot(tech_salary_boxplot_data, aes(x = salary_usd, y = reorder(country, salary_usd))) +
geom_boxplot() +
labs( title= " Tech salaries across USA, Australia, Germany, UK, Canada",
x="Salary (USD)",
y="Countries")
tech_salary_boxplot
According to the box plot, USA has the highest mean Computing and Tech Salary. USA also has high variability, since it is known as the tech hub of the world which leads to some salaries being extremely high.
mean_median_tech_salary_by_country <- ask_a_manager_2021_cleaned %>%
filter(!is.na(salary_usd)) %>%
filter(country_code %in% top5_country ) %>%
filter(industry_cleaned == "Computing or Tech") %>%
group_by(country_code) %>%
summarise(mean_tech_salary = mean(salary_usd),
median_tech_salary = median(salary_usd)) %>%
arrange(desc(mean_tech_salary))
mean_median_tech_salary_by_country| country_code | mean_tech_salary | median_tech_salary |
|---|---|---|
| USA | 1.3e+05 | 1.21e+05 |
| DEU | 1.09e+05 | 8.81e+04 |
| AUS | 1.09e+05 | 9.44e+04 |
| GBR | 9.35e+04 | 7.74e+04 |
| CAN | 8.43e+04 | 7.99e+04 |
5.2.1 Confidence interval of Median Salaries
set.seed(123)
median_ci_median_tech <- data.frame(matrix(ncol = 4,nrow = 0))
colnames(median_ci_median_tech)<-c("country","lower_ci", "upper_ci","median")
median_ci_median_tech$country <- as.character(median_ci_median_tech$country)
for(i in top5_country)
{
#confidence interval bootstrap
median_tech_country <- ask_a_manager_2021_cleaned%>%
filter(country_code == i) %>%
filter(!is.na(salary_usd)) %>%
filter(industry_cleaned == "Computing or Tech") %>%
group_by(country)%>%
summarise(median = median(salary_usd))
boot_dist <- ask_a_manager_2021_cleaned%>%
filter(country_code == i) %>%
filter(!is.na(salary_usd)) %>%
filter(industry_cleaned == "Computing or Tech") %>%
specify(response = salary_usd) %>%
# Generate bootstrap samples
generate(reps = 1000, type = "bootstrap") %>%
# Calculate mean of each bootstrap sample
calculate(stat = "median")
#creating the CI table with country code
median_ci <- get_ci(boot_dist , level = 0.95, type ="percentile")
median_ci_median_tech <- median_ci_median_tech%>%
add_row(country = i,
lower_ci = median_ci$lower_ci,
upper_ci = median_ci$upper_ci,
median = median_tech_country$median)
#visualisation of the resulting bootstrap distribution and the CIs
fig<- visualize(boot_dist) +
shade_ci(endpoints = median_ci, fill="khaki")+
labs(title = sprintf("Simulation of Bootstrap Distribution for Median %s",i))
plot(fig)
}
5.2.2 Confidence interval of the Mean Salaries
mean_conf_country_tech <- ask_a_manager_2021_cleaned%>%
filter(industry_cleaned == "Computing or Tech",
!is.na(salary_usd))%>%
filter(country_code %in% top5_country)%>%
group_by(country)%>%
summarise(mean_salary = mean(salary_usd, na.rm=TRUE),
n_salary = n(),
sd_salary = sd(salary_usd, na.rm = TRUE),
se_salary = sd_salary/sqrt(n_salary),
t_critical = qt(0.975, n_salary - 1),
salary_lower = mean_salary - t_critical*se_salary,
salary_upper = mean_salary + t_critical*se_salary)#Graph of CIs:
graph <- ggplot(mean_conf_country_tech, aes(colour = country)) +
geom_errorbar(aes(xmin = salary_lower, xmax = salary_upper, y= reorder(country, mean_salary)), width = 0.1, size = 1.5) +
geom_point(aes(x=mean_salary, y=reorder(country, mean_salary)), size = 3 ) +
labs(title="Confidence Intervals of Mean Salary by Country in Computing and Tech Industry",
subtitle="95% confidence intervals",
x="Mean Salary (USD)",
y =" ") +
theme_bw() +
geom_text(aes(label =format(round(mean_salary,0), big.mark = ","), x = round(mean_salary,0), y=country), size = 3, color="black", hjust = 0.5, vjust = 2, nudge_x = 0.00, nudge_y = 0.00)
graph
# Median CI
graph <- ggplot(median_ci_median_tech, aes(colour = country)) +
geom_errorbar(aes(xmin = lower_ci, xmax = upper_ci, y= reorder(country, median)), width = 0.1, size = 1.5) +
geom_point(aes(x=median, y=reorder(country, median)), size = 3 ) +
labs(title="Confidence Intervals of Median Salary by Country in Computing and Tech Industry",
subtitle="95% confidence intervals",
x="Median Salary (USD)",
y =" ") +
theme_bw() +
geom_text(aes(label =format(round(median,0), big.mark = ","), x = round(median,0), y=country), size = 3, color="black", hjust = 0.5, vjust = 2, nudge_x = 0.00, nudge_y = 0.00)
graph
We created this graph in order to have an overall understanding of the selected countries’ mean salaries in tech and the confidence intervals of their mean. Because USA has more data points, its confidence interval is narrower.
There are a lot more significant differences in the median salary between the countries the US clearly leading the way with its confidence interval not overlaping with any of the other countries.
According to the graph, USA and Australia are leading the tech industry in the world in terms of salaries. Is there an actual difference in the mean salaries for tech industry in USA and Australia? In order to proceed, we will conduct hypothesis testing.
H0: There’s no difference between the salaries of US and Germany in the Computing and Tech industry. H1: There’s a difference between the salaries of US and Germany in the Computing and Tech industry.
Use the infer package for hypothesis testing:
tech_usa_aus <- ask_a_manager_2021_cleaned %>%
filter( country_code %in% c("AUS" , "USA")) %>%
filter(!is.na(salary_usd), industry_cleaned == "Computing or Tech")
t.test(salary_usd ~ country_code, tech_usa_aus)##
## Welch Two Sample t-test
##
## data: salary_usd by country_code
## t = -2, df = 53, p-value = 0.1
## alternative hypothesis: true difference in means between group AUS and group USA is not equal to 0
## 95 percent confidence interval:
## -46821 4863
## sample estimates:
## mean in group AUS mean in group USA
## 109230 130209According to the hypothesis testing, we cannot reject the null hypothesis that there is no difference between the mean salaries for tech employees in USA and Australia.
However there is a clear difference in median salaries between the countries where the USA is a clear leader.
So, since USA dominates the Tech industry, we would like to analyse the factors that affect the tech salary like income and years of of experience at work.
Conclusion: US is the place to be for Tech!
5.3 Salary and Gender relationship in Tech Industry
Let us analyse the tech industry further. The countries where the analyses should be conducted are the United States, Canada and the UK figure @ref(fig:num-res-per-country) which shows USA, Canda and UH have the highest samples within the dataset. Referring to the figure (fig:gender-industry-country) we can see that the Tech industry has the most balanced gender data across all countries in the dataset.
Based on this knowledge let us compare the mean and median salaries of man and women within the industry.
5.3.1 Mean Salary in tech Man and Woman t-test
salary_in_tech_by_gender_tech <- ask_a_manager_2021_cleaned %>%
filter(industry_cleaned == "Computing or Tech") %>%
filter(country_code == "USA") %>%
filter(gender %in% c("Man", "Woman"))
t.test(salary_usd ~ gender, data = salary_in_tech_by_gender_tech)##
## Welch Two Sample t-test
##
## data: salary_usd by gender
## t = 11, df = 2426, p-value <2e-16
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## 22883 32727
## sample estimates:
## mean in group Man mean in group Woman
## 146559 118754There is a significant difference in the mean salaries of men and woman in the US in the Computing/Tech industry. Tech industry and analytical roles have been dominated by males in the past century. That’s why, males might have had the opportunity to get promoted and receive higher salaries. People with high paying roles tend to have 30+ of experience, and there are not many women in tech that have that much experience.
5.3.2 Median Confidence Interval
set.seed(123)
median_ci_median_tech_gender <- data.frame(matrix(ncol = 5,nrow = 0))
colnames(median_ci_median_tech_gender)<-c("country","gender","lower_ci", "upper_ci","median")
median_ci_median_tech_gender$country <- as.character(median_ci_median_tech_gender$country)
median_ci_median_tech_gender$gender <- as.character(median_ci_median_tech_gender$gender)
for(i in top5_country[1:3])
{
for(g in c("Man","Woman"))
{ #confidence interval bootstrap
median_tech_country <- ask_a_manager_2021_cleaned%>%
filter(country_code == i, gender == g) %>%
filter(!is.na(salary_usd)) %>%
filter(industry_cleaned == "Computing or Tech") %>%
group_by(country)%>%
summarise(median = median(salary_usd))
boot_dist <- ask_a_manager_2021_cleaned%>%
filter(country_code == i, gender == g) %>%
filter(!is.na(salary_usd)) %>%
filter(industry_cleaned == "Computing or Tech") %>%
specify(response = salary_usd) %>%
# Generate bootstrap samples
generate(reps = 1000, type = "bootstrap") %>%
# Calculate mean of each bootstrap sample
calculate(stat = "median")
#creating the CI table with country code
median_ci <- get_ci(boot_dist , level = 0.95, type ="percentile")
median_ci_median_tech_gender <- median_ci_median_tech_gender%>%
add_row(country = i,
gender = g,
lower_ci = median_ci$lower_ci,
upper_ci = median_ci$upper_ci,
median = median_tech_country$median)
#visualisation of the resulting bootstrap distribution and the CIs
fig<- visualize(boot_dist) +
shade_ci(endpoints = median_ci, fill="khaki")+
labs(title = sprintf("Simulation of Bootstrap Distribution for Median %s",i))
plot(fig)}
}
# Median CI
graph <- ggplot(median_ci_median_tech_gender, aes(colour = gender)) +
geom_errorbar(aes(xmin = lower_ci, xmax = upper_ci, y= reorder(gender, median)), width = 0.1, size = 1.5) +
geom_point(aes(x=median, y=reorder(gender, median)), size = 3 ) +
labs(title="Confidence Intervals of Median Salary by Country in Computing and Tech Industry",
subtitle="95% confidence intervals",
x="Median Salary (USD)",
y =" ") +
theme_bw() +
geom_text(aes(label =format(round(median,0), big.mark = ","), x = round(median,0), y=gender), size = 3, color="black", hjust = 0.5, vjust = 2, nudge_x = 0.00, nudge_y = 0.00)+
facet_wrap(~country)+
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 20, hjust = 0.7))+
scale_x_continuous(labels = function(median) sprintf("%s$",format(median, big.mark = ",")))
graph
set.seed(123)
for(c in top5_country[1:3])
{
diff_prop <- ask_a_manager_2021_cleaned %>%
filter(industry_cleaned == "Computing or Tech") %>%
filter(country_code == c) %>%
filter(gender %in% c("Man", "Woman"))%>%
specify(salary_usd ~ gender) %>%
calculate("diff in medians",
order = c("Man", "Woman"))
diff_prop
salary_median_tech_null <- ask_a_manager_2021_cleaned %>%
filter(industry_cleaned == "Computing or Tech") %>%
filter(country_code == "USA") %>%
filter(gender %in% c("Man", "Woman"))%>%
specify(salary_usd ~ gender) %>%
hypothesise(null = "independence")%>%
generate(reps = 5000, type = "permute" )%>%
calculate("diff in medians", order= c("Man", "Woman"))
fig <-salary_median_tech_null%>%
visualise()+
geom_vline(xintercept = diff_prop$stat, color = "#FF4136", size = 1) +
labs(x = "Difference in median proportion\n(Man − Women in Tech)",
y = "Count",
subtitle = sprintf("Red line shows observed difference in median proportions for country: %s",c )) +
scale_x_continuous(labels = percent_format(accuracy = 1)) +
theme_minimal() +
theme(panel.grid.minor = element_blank())
plot(fig)
}
We observe statitically significant differences within the tech industry between the median salary of men and women.
5.4 Salary and Gender Relationship in other industries
salary_in_tech_by_gender_hc <- ask_a_manager_2021_cleaned %>%
filter(industry_cleaned == "Health care") %>%
filter(country_code == "USA") %>%
filter(gender %in% c("Man", "Woman"))
t.test(salary_usd ~ gender, data = salary_in_tech_by_gender_hc )##
## Welch Two Sample t-test
##
## data: salary_usd by gender
## t = 2, df = 215, p-value = 0.04
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## 533 22406
## sample estimates:
## mean in group Man mean in group Woman
## 106810 95340It is very interesting that there is not a significant difference in the mean salaries of men and women in the health care industry! So we fail to reject the null hypothesis. This makes sense since health care is a more traditional profession, so the percentage of women in health care is higher than the percentage of women in tech. This brings up questions on whether or not different industries pay different genders differently!
salary_in_tech_by_gender_bank <- ask_a_manager_2021_cleaned %>%
filter(industry_cleaned == "Accounting, Banking & Finance") %>%
filter(country_code == "USA") %>%
filter(gender %in% c("Man", "Woman"))
t.test(salary_usd ~ gender, data = salary_in_tech_by_gender_bank )##
## Welch Two Sample t-test
##
## data: salary_usd by gender
## t = 5, df = 246, p-value = 3e-06
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
## 15505 37126
## sample estimates:
## mean in group Man mean in group Woman
## 114681 88366In the Accounting, Banking & Finance industry, there is still a significant difference between the mean salaries of women and men. This is not suprising since the finance and banking industry has been dominated by males for a century.
6 Linear Model
names(ask_a_manager_2021_cleaned_gender)## [1] "currency"
## [2] "timestamp"
## [3] "how_old_are_you"
## [4] "industry"
## [5] "job_title"
## [6] "additional_context_on_job_title"
## [7] "annual_salary"
## [8] "other_monetary_comp"
## [9] "currency_other"
## [10] "additional_context_on_income"
## [11] "country"
## [12] "state"
## [13] "city"
## [14] "overall_years_of_professional_experience"
## [15] "years_of_experience_in_field"
## [16] "highest_level_of_education_completed"
## [17] "gender"
## [18] "race"
## [19] "country_code"
## [20] "match_currency"
## [21] "check_match"
## [22] "exchange_matching_check"
## [23] "exrate_to_usd"
## [24] "salary_usd"
## [25] "industry_cleaned"
## [26] "industry_lower"
## [27] "isco_group"
## [28] "preferred_label"ask_lm <- lm(salary_usd ~ country ,
data=ask_a_manager_2021_cleaned_gender, )
ask_lm %>%
get_regression_table()| term | estimate | std_error | statistic | p_value | lower_ci | upper_ci |
|---|---|---|---|---|---|---|
| intercept | 5.61e+04 | 7.23e+04 | 0.776 | 0.438 | -8.56e+04 | 1.98e+05 |
| country: Argentina | -4.83e+04 | 7.73e+04 | -0.625 | 0.532 | -2e+05 | 1.03e+05 |
| country: Australia | 2.35e+04 | 7.24e+04 | 0.325 | 0.745 | -1.18e+05 | 1.65e+05 |
| country: Austria | 1.42e+04 | 7.46e+04 | 0.19 | 0.85 | -1.32e+05 | 1.6e+05 |
| country: Bahamas | 9.91e+03 | 1.02e+05 | 0.097 | 0.923 | -1.9e+05 | 2.1e+05 |
| country: Bangladesh | -1.61e+04 | 8.85e+04 | -0.182 | 0.856 | -1.9e+05 | 1.57e+05 |
| country: Belgium | 1.91e+04 | 7.34e+04 | 0.26 | 0.795 | -1.25e+05 | 1.63e+05 |
| country: Bermuda | 5.27e+04 | 8.85e+04 | 0.595 | 0.552 | -1.21e+05 | 2.26e+05 |
| country: Brazil | -1.8e+04 | 7.62e+04 | -0.236 | 0.814 | -1.67e+05 | 1.31e+05 |
| country: Bulgaria | -8.82e+03 | 8.85e+04 | -0.1 | 0.921 | -1.82e+05 | 1.65e+05 |
| country: Cambodia | -3.21e+04 | 1.02e+05 | -0.314 | 0.754 | -2.32e+05 | 1.68e+05 |
| country: Canada | 9.42e+03 | 7.23e+04 | 0.13 | 0.896 | -1.32e+05 | 1.51e+05 |
| country: Cayman Islands | 2.49e+04 | 1.02e+05 | 0.244 | 0.807 | -1.75e+05 | 2.25e+05 |
| country: Chile | -1.81e+04 | 1.02e+05 | -0.177 | 0.86 | -2.18e+05 | 1.82e+05 |
| country: China | -5.84e+03 | 7.67e+04 | -0.076 | 0.939 | -1.56e+05 | 1.44e+05 |
| country: Colombia | -2.95e+04 | 1.02e+05 | -0.289 | 0.773 | -2.3e+05 | 1.71e+05 |
| country: Congo - Brazzaville | 3.48e+04 | 1.02e+05 | 0.34 | 0.734 | -1.66e+05 | 2.35e+05 |
| country: Costa Rica | 2.19e+04 | 1.02e+05 | 0.214 | 0.83 | -1.78e+05 | 2.22e+05 |
| country: Côte d’Ivoire | 1.14e+04 | 1.02e+05 | 0.112 | 0.911 | -1.89e+05 | 2.12e+05 |
| country: Croatia | -1.38e+04 | 1.02e+05 | -0.135 | 0.893 | -2.14e+05 | 1.87e+05 |
| country: Cuba | -4.65e+04 | 1.02e+05 | -0.455 | 0.649 | -2.47e+05 | 1.54e+05 |
| country: Czechia | -1.97e+04 | 7.81e+04 | -0.253 | 0.801 | -1.73e+05 | 1.33e+05 |
| country: Denmark | 3.91e+04 | 7.39e+04 | 0.529 | 0.597 | -1.06e+05 | 1.84e+05 |
| country: Ecuador | 2.79e+04 | 1.02e+05 | 0.273 | 0.785 | -1.72e+05 | 2.28e+05 |
| country: Eritrea | 4.19e+04 | 1.02e+05 | 0.41 | 0.682 | -1.58e+05 | 2.42e+05 |
| country: Estonia | -2.42e+04 | 8.85e+04 | -0.273 | 0.785 | -1.98e+05 | 1.49e+05 |
| country: Finland | -7.68e+03 | 7.45e+04 | -0.103 | 0.918 | -1.54e+05 | 1.38e+05 |
| country: France | 1.15e+04 | 7.29e+04 | 0.158 | 0.874 | -1.31e+05 | 1.54e+05 |
| country: Germany | 3.5e+04 | 7.25e+04 | 0.484 | 0.629 | -1.07e+05 | 1.77e+05 |
| country: Ghana | -3.74e+03 | 8.85e+04 | -0.042 | 0.966 | -1.77e+05 | 1.7e+05 |
| country: Greece | -3.05e+04 | 7.92e+04 | -0.385 | 0.7 | -1.86e+05 | 1.25e+05 |
| country: Hong Kong SAR China | 1.6e+03 | 8.08e+04 | 0.02 | 0.984 | -1.57e+05 | 1.6e+05 |
| country: Hungary | 2.52e+04 | 1.02e+05 | 0.247 | 0.805 | -1.75e+05 | 2.26e+05 |
| country: India | -2.81e+04 | 7.48e+04 | -0.376 | 0.707 | -1.75e+05 | 1.18e+05 |
| country: Indonesia | -1.92e+04 | 8.85e+04 | -0.217 | 0.828 | -1.93e+05 | 1.54e+05 |
| country: Ireland | 1.5e+04 | 7.26e+04 | 0.206 | 0.837 | -1.27e+05 | 1.57e+05 |
| country: Isle of Man | 6.16e+04 | 1.02e+05 | 0.602 | 0.547 | -1.39e+05 | 2.62e+05 |
| country: Israel | 3.28e+04 | 7.48e+04 | 0.439 | 0.661 | -1.14e+05 | 1.79e+05 |
| country: Italy | 8.48e+03 | 7.48e+04 | 0.113 | 0.91 | -1.38e+05 | 1.55e+05 |
| country: Jamaica | -1.11e+04 | 1.02e+05 | -0.108 | 0.914 | -2.11e+05 | 1.89e+05 |
| country: Japan | 301 | 7.35e+04 | 0.004 | 0.997 | -1.44e+05 | 1.44e+05 |
| country: Jordan | -2.21e+04 | 1.02e+05 | -0.216 | 0.829 | -2.22e+05 | 1.78e+05 |
| country: Kenya | 1.91e+03 | 8.85e+04 | 0.022 | 0.983 | -1.72e+05 | 1.75e+05 |
| country: Kuwait | -4.25e+04 | 1.02e+05 | -0.416 | 0.677 | -2.43e+05 | 1.58e+05 |
| country: Latvia | -2.16e+04 | 8.35e+04 | -0.259 | 0.796 | -1.85e+05 | 1.42e+05 |
| country: Lithuania | -5.18e+03 | 8.85e+04 | -0.059 | 0.953 | -1.79e+05 | 1.68e+05 |
| country: Luxembourg | 5.59e+04 | 8.35e+04 | 0.669 | 0.503 | -1.08e+05 | 2.19e+05 |
| country: Malaysia | -2.13e+04 | 8.08e+04 | -0.263 | 0.792 | -1.8e+05 | 1.37e+05 |
| country: Malta | -6.77e+03 | 1.02e+05 | -0.066 | 0.947 | -2.07e+05 | 1.94e+05 |
| country: Mexico | -7.7e+03 | 7.67e+04 | -0.1 | 0.92 | -1.58e+05 | 1.43e+05 |
| country: Morocco | -2.81e+04 | 1.02e+05 | -0.275 | 0.783 | -2.28e+05 | 1.72e+05 |
| country: Netherlands | 1.82e+04 | 7.27e+04 | 0.251 | 0.802 | -1.24e+05 | 1.61e+05 |
| country: New Zealand | 1.72e+04 | 7.26e+04 | 0.237 | 0.812 | -1.25e+05 | 1.6e+05 |
| country: Nigeria | -4.87e+04 | 1.02e+05 | -0.476 | 0.634 | -2.49e+05 | 1.52e+05 |
| country: Norway | 3e+04 | 7.5e+04 | 0.4 | 0.689 | -1.17e+05 | 1.77e+05 |
| country: Pakistan | -1.86e+04 | 8.35e+04 | -0.223 | 0.824 | -1.82e+05 | 1.45e+05 |
| country: Panama | -2.85e+04 | 1.02e+05 | -0.279 | 0.78 | -2.29e+05 | 1.72e+05 |
| country: Philippines | -3.56e+04 | 7.67e+04 | -0.464 | 0.643 | -1.86e+05 | 1.15e+05 |
| country: Poland | -5.7e+03 | 8.35e+04 | -0.068 | 0.946 | -1.69e+05 | 1.58e+05 |
| country: Portugal | 3.08e+03 | 7.92e+04 | 0.039 | 0.969 | -1.52e+05 | 1.58e+05 |
| country: Puerto Rico | -8.09e+03 | 8.08e+04 | -0.1 | 0.92 | -1.66e+05 | 1.5e+05 |
| country: Qatar | 1.19e+04 | 1.02e+05 | 0.117 | 0.907 | -1.88e+05 | 2.12e+05 |
| country: Romania | 6.41e+03 | 8.08e+04 | 0.079 | 0.937 | -1.52e+05 | 1.65e+05 |
| country: Russia | -4.11e+04 | 1.02e+05 | -0.402 | 0.688 | -2.41e+05 | 1.59e+05 |
| country: Rwanda | 8.91e+03 | 1.02e+05 | 0.087 | 0.931 | -1.91e+05 | 2.09e+05 |
| country: Saudi Arabia | -1.26e+04 | 1.02e+05 | -0.123 | 0.902 | -2.13e+05 | 1.88e+05 |
| country: Serbia | -2.86e+04 | 1.02e+05 | -0.28 | 0.78 | -2.29e+05 | 1.72e+05 |
| country: Sierra Leone | 4.09e+04 | 1.02e+05 | 0.4 | 0.689 | -1.59e+05 | 2.41e+05 |
| country: Singapore | 2.16e+05 | 7.67e+04 | 2.81 | 0.005 | 6.54e+04 | 3.66e+05 |
| country: Slovakia | 1.08e+05 | 1.02e+05 | 1.06 | 0.289 | -9.2e+04 | 3.09e+05 |
| country: Slovenia | -3.57e+04 | 1.02e+05 | -0.349 | 0.727 | -2.36e+05 | 1.65e+05 |
| country: Somalia | 1.91e+03 | 1.02e+05 | 0.019 | 0.985 | -1.98e+05 | 2.02e+05 |
| country: South Africa | -1.27e+04 | 7.44e+04 | -0.171 | 0.864 | -1.59e+05 | 1.33e+05 |
| country: South Korea | -2.71e+04 | 7.92e+04 | -0.342 | 0.732 | -1.82e+05 | 1.28e+05 |
| country: Spain | 149 | 7.31e+04 | 0.002 | 0.998 | -1.43e+05 | 1.43e+05 |
| country: Sri Lanka | 3.52e+05 | 8.85e+04 | 3.97 | 0 | 1.78e+05 | 5.25e+05 |
| country: Sweden | 8e+03 | 7.32e+04 | 0.109 | 0.913 | -1.36e+05 | 1.52e+05 |
| country: Switzerland | 6.5e+04 | 7.33e+04 | 0.887 | 0.375 | -7.87e+04 | 2.09e+05 |
| country: Taiwan | -2.59e+04 | 8.35e+04 | -0.311 | 0.756 | -1.9e+05 | 1.38e+05 |
| country: Thailand | -1.91e+04 | 8.08e+04 | -0.236 | 0.813 | -1.77e+05 | 1.39e+05 |
| country: Uganda | 4.39e+04 | 1.02e+05 | 0.43 | 0.667 | -1.56e+05 | 2.44e+05 |
| country: Ukraine | -4.41e+04 | 1.02e+05 | -0.431 | 0.666 | -2.44e+05 | 1.56e+05 |
| country: United Arab Emirates | 2.74e+04 | 8.85e+04 | 0.31 | 0.757 | -1.46e+05 | 2.01e+05 |
| country: United Kingdom | 1.21e+04 | 7.23e+04 | 0.168 | 0.867 | -1.3e+05 | 1.54e+05 |
| country: United States | 3.63e+04 | 7.23e+04 | 0.503 | 0.615 | -1.05e+05 | 1.78e+05 |
| country: Uruguay | 2.79e+04 | 1.02e+05 | 0.273 | 0.785 | -1.72e+05 | 2.28e+05 |
| country: Vietnam | -4.06e+04 | 8.85e+04 | -0.459 | 0.646 | -2.14e+05 | 1.33e+05 |
| country: Zimbabwe | -6.09e+03 | 1.02e+05 | -0.06 | 0.953 | -2.06e+05 | 1.94e+05 |
ask_lm %>%
get_regression_summaries()| r_squared | adj_r_squared | mse | rmse | sigma | statistic | p_value | df | nobs |
|---|---|---|---|---|---|---|---|---|
| 0.022 | 0.018 | 5.21e+09 | 7.22e+04 | 7.23e+04 | 6.74 | 0 | 87 | 2.65e+04 |
ask_a_manager_2021_cleaned_gender_country <- ask_a_manager_2021_cleaned_gender %>%
filter(country == "United States")
ask_lm <- lm(salary_usd ~ gender +
race +
overall_years_of_professional_experience +
how_old_are_you + industry +
highest_level_of_education_completed,
data=ask_a_manager_2021_cleaned_gender_country )
ask_lm %>%
get_regression_table()| term | estimate | std_error | statistic | p_value | lower_ci | upper_ci |
|---|---|---|---|---|---|---|
| intercept | 7.95e+04 | 7.04e+04 | 1.13 | 0.258 | -5.84e+04 | 2.17e+05 |
| gender: Non-binary | -1.63e+04 | 3.27e+03 | -4.98 | 0 | -2.27e+04 | -9.88e+03 |
| gender: Other or prefer not to answer | -1.89e+04 | 5.26e+03 | -3.58 | 0 | -2.92e+04 | -8.54e+03 |
| gender: Woman | -1.8e+04 | 1.35e+03 | -13.3 | 0 | -2.07e+04 | -1.54e+04 |
| race: Asian or Asian American | 4.92e+03 | 4.33e+03 | 1.14 | 0.256 | -3.56e+03 | 1.34e+04 |
| race: Asian or Asian American, Another option not listed here or prefer not to answer | 1.8e+04 | 3.16e+04 | 0.57 | 0.569 | -4.39e+04 | 7.99e+04 |
| race: Asian or Asian American, Black or African American | -9.71e+03 | 2.51e+04 | -0.387 | 0.699 | -5.89e+04 | 3.95e+04 |
| race: Asian or Asian American, Black or African American, Hispanic, Latino, or Spanish origin | -3.57e+04 | 7.03e+04 | -0.508 | 0.612 | -1.74e+05 | 1.02e+05 |
| race: Asian or Asian American, Black or African American, Native American or Alaska Native, White | 2.24e+03 | 7.03e+04 | 0.032 | 0.975 | -1.36e+05 | 1.4e+05 |
| race: Asian or Asian American, Black or African American, White | -7.59e+03 | 3.54e+04 | -0.215 | 0.83 | -7.69e+04 | 6.17e+04 |
| race: Asian or Asian American, Hispanic, Latino, or Spanish origin | 4.11e+03 | 2.06e+04 | 0.2 | 0.842 | -3.63e+04 | 4.45e+04 |
| race: Asian or Asian American, Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer | -2.38e+03 | 7.02e+04 | -0.034 | 0.973 | -1.4e+05 | 1.35e+05 |
| race: Asian or Asian American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White | 5.28e+03 | 7.03e+04 | 0.075 | 0.94 | -1.33e+05 | 1.43e+05 |
| race: Asian or Asian American, Hispanic, Latino, or Spanish origin, White | -9.96e+03 | 2.41e+04 | -0.413 | 0.68 | -5.72e+04 | 3.73e+04 |
| race: Asian or Asian American, Middle Eastern or Northern African | 3.25e+04 | 3.18e+04 | 1.02 | 0.306 | -2.97e+04 | 9.48e+04 |
| race: Asian or Asian American, Middle Eastern or Northern African, White | 3.96e+03 | 7.02e+04 | 0.056 | 0.955 | -1.34e+05 | 1.42e+05 |
| race: Asian or Asian American, Native American or Alaska Native | 8.35e+05 | 7.02e+04 | 11.9 | 0 | 6.97e+05 | 9.72e+05 |
| race: Asian or Asian American, Native American or Alaska Native, White | -7.13e+03 | 3.53e+04 | -0.202 | 0.84 | -7.64e+04 | 6.21e+04 |
| race: Asian or Asian American, White | -2.28e+03 | 5.62e+03 | -0.405 | 0.686 | -1.33e+04 | 8.74e+03 |
| race: Asian or Asian American, White, Another option not listed here or prefer not to answer | -2.37e+04 | 3.53e+04 | -0.673 | 0.501 | -9.29e+04 | 4.54e+04 |
| race: Black or African American | 1.31e+03 | 4.78e+03 | 0.275 | 0.783 | -8.05e+03 | 1.07e+04 |
| race: Black or African American, Another option not listed here or prefer not to answer | -3.03e+03 | 7.03e+04 | -0.043 | 0.966 | -1.41e+05 | 1.35e+05 |
| race: Black or African American, Hispanic, Latino, or Spanish origin | 1.06e+04 | 1.52e+04 | 0.699 | 0.484 | -1.92e+04 | 4.04e+04 |
| race: Black or African American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White | -1.67e+04 | 4.07e+04 | -0.411 | 0.681 | -9.65e+04 | 6.3e+04 |
| race: Black or African American, Hispanic, Latino, or Spanish origin, White | 2.95e+04 | 2.51e+04 | 1.18 | 0.24 | -1.97e+04 | 7.88e+04 |
| race: Black or African American, Middle Eastern or Northern African | -8.76e+03 | 4.97e+04 | -0.176 | 0.86 | -1.06e+05 | 8.87e+04 |
| race: Black or African American, Middle Eastern or Northern African, Native American or Alaska Native, White | 2.09e+03 | 7.02e+04 | 0.03 | 0.976 | -1.36e+05 | 1.4e+05 |
| race: Black or African American, Middle Eastern or Northern African, White | -4.31e+03 | 4.07e+04 | -0.106 | 0.916 | -8.4e+04 | 7.54e+04 |
| race: Black or African American, Native American or Alaska Native, White | -2e+04 | 4.98e+04 | -0.402 | 0.688 | -1.18e+05 | 7.75e+04 |
| race: Black or African American, White | 2.62e+03 | 7.75e+03 | 0.338 | 0.736 | -1.26e+04 | 1.78e+04 |
| race: Hispanic, Latino, or Spanish origin | -9.3e+03 | 4.98e+03 | -1.87 | 0.062 | -1.91e+04 | 465 |
| race: Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer | -7.81e+03 | 2.9e+04 | -0.269 | 0.788 | -6.46e+04 | 4.9e+04 |
| race: Hispanic, Latino, or Spanish origin, Middle Eastern or Northern African, White | -1.28e+04 | 3.54e+04 | -0.363 | 0.717 | -8.22e+04 | 5.65e+04 |
| race: Hispanic, Latino, or Spanish origin, Native American or Alaska Native | -9.92e+03 | 2.37e+04 | -0.419 | 0.675 | -5.63e+04 | 3.65e+04 |
| race: Hispanic, Latino, or Spanish origin, Native American or Alaska Native, Another option not listed here or prefer not to answer | -6.83e+04 | 7.02e+04 | -0.972 | 0.331 | -2.06e+05 | 6.94e+04 |
| race: Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White | 7.4e+03 | 2.51e+04 | 0.295 | 0.768 | -4.17e+04 | 5.65e+04 |
| race: Hispanic, Latino, or Spanish origin, White | -1.56e+04 | 5.4e+03 | -2.88 | 0.004 | -2.61e+04 | -4.99e+03 |
| race: Hispanic, Latino, or Spanish origin, White, Another option not listed here or prefer not to answer | -5.35e+04 | 7.03e+04 | -0.761 | 0.447 | -1.91e+05 | 8.43e+04 |
| race: Middle Eastern or Northern African | -4.06e+03 | 1.15e+04 | -0.354 | 0.723 | -2.66e+04 | 1.84e+04 |
| race: Middle Eastern or Northern African, Native American or Alaska Native, White | -3.78e+04 | 7.02e+04 | -0.539 | 0.59 | -1.75e+05 | 9.98e+04 |
| race: Middle Eastern or Northern African, White | -4.15e+03 | 9.16e+03 | -0.453 | 0.65 | -2.21e+04 | 1.38e+04 |
| race: Middle Eastern or Northern African, White, Another option not listed here or prefer not to answer | 1.72e+03 | 7.02e+04 | 0.024 | 0.98 | -1.36e+05 | 1.39e+05 |
| race: Native American or Alaska Native | -1.18e+04 | 1.26e+04 | -0.931 | 0.352 | -3.66e+04 | 1.3e+04 |
| race: Native American or Alaska Native, White | -8.7e+03 | 1.07e+04 | -0.815 | 0.415 | -2.96e+04 | 1.22e+04 |
| race: White | -1.17e+04 | 3.71e+03 | -3.15 | 0.002 | -1.89e+04 | -4.4e+03 |
| race: White, Another option not listed here or prefer not to answer | -2.18e+04 | 1.33e+04 | -1.63 | 0.103 | -4.79e+04 | 4.39e+03 |
| overall_years_of_professional_experience: 11 - 20 years | 2.83e+04 | 4.51e+03 | 6.29 | 0 | 1.95e+04 | 3.72e+04 |
| overall_years_of_professional_experience: 2 - 4 years | -735 | 4.31e+03 | -0.171 | 0.865 | -9.18e+03 | 7.71e+03 |
| overall_years_of_professional_experience: 21 - 30 years | 3.7e+04 | 4.85e+03 | 7.62 | 0 | 2.75e+04 | 4.65e+04 |
| overall_years_of_professional_experience: 31 - 40 years | 4.25e+04 | 5.84e+03 | 7.29 | 0 | 3.11e+04 | 5.4e+04 |
| overall_years_of_professional_experience: 41 years or more | 2.63e+04 | 9.44e+03 | 2.79 | 0.005 | 7.82e+03 | 4.48e+04 |
| overall_years_of_professional_experience: 5-7 years | 8.68e+03 | 4.41e+03 | 1.97 | 0.049 | 32.8 | 1.73e+04 |
| overall_years_of_professional_experience: 8 - 10 years | 1.83e+04 | 4.43e+03 | 4.13 | 0 | 9.6e+03 | 2.7e+04 |
| how_old_are_you: 25-34 | 6.78e+03 | 3.02e+03 | 2.24 | 0.025 | 851 | 1.27e+04 |
| how_old_are_you: 35-44 | 6.51e+03 | 3.36e+03 | 1.94 | 0.053 | -79.4 | 1.31e+04 |
| how_old_are_you: 45-54 | 3.96e+03 | 3.85e+03 | 1.03 | 0.303 | -3.58e+03 | 1.15e+04 |
| how_old_are_you: 55-64 | 1.04e+04 | 4.86e+03 | 2.15 | 0.032 | 909 | 2e+04 |
| how_old_are_you: 65 or over | 1.04e+04 | 9.55e+03 | 1.09 | 0.278 | -8.34e+03 | 2.91e+04 |
| how_old_are_you: under 18 | 1.84e+04 | 2.51e+04 | 0.734 | 0.463 | -3.07e+04 | 6.75e+04 |
| industry: Academia - STEM | -2.38e+04 | 9.92e+04 | -0.24 | 0.81 | -2.18e+05 | 1.71e+05 |
| industry: Academia / Research | -2.96e+04 | 9.92e+04 | -0.299 | 0.765 | -2.24e+05 | 1.65e+05 |
| industry: Academia--cell and molecular biology | -5.35e+04 | 9.92e+04 | -0.54 | 0.589 | -2.48e+05 | 1.41e+05 |
| industry: Academic Medicine | -3.79e+04 | 9.92e+04 | -0.382 | 0.702 | -2.32e+05 | 1.56e+05 |
| industry: Academic Press Production | -4.58e+04 | 9.92e+04 | -0.462 | 0.644 | -2.4e+05 | 1.49e+05 |
| industry: academic publishing | -2.1e+04 | 9.92e+04 | -0.212 | 0.832 | -2.16e+05 | 1.73e+05 |
| industry: Academic publishing | -4.47e+04 | 8.1e+04 | -0.552 | 0.581 | -2.03e+05 | 1.14e+05 |
| industry: Academic Publishing | -1.94e+04 | 9.92e+04 | -0.195 | 0.845 | -2.14e+05 | 1.75e+05 |
| industry: academic research | -2.81e+04 | 9.92e+04 | -0.283 | 0.777 | -2.23e+05 | 1.66e+05 |
| industry: Academic research | -3.7e+04 | 8.1e+04 | -0.457 | 0.648 | -1.96e+05 | 1.22e+05 |
| industry: Academic research (Psychology) | -1.9e+04 | 9.92e+04 | -0.191 | 0.848 | -2.13e+05 | 1.75e+05 |
| industry: Academic research (social science) | -2.41e+04 | 9.92e+04 | -0.243 | 0.808 | -2.18e+05 | 1.7e+05 |
| industry: Academic science | -5.6e+04 | 8.59e+04 | -0.652 | 0.515 | -2.24e+05 | 1.12e+05 |
| industry: Academic Scientific Research | -2.97e+04 | 9.91e+04 | -0.299 | 0.765 | -2.24e+05 | 1.65e+05 |
| industry: Academic/nonprofit research | -1.24e+04 | 9.92e+04 | -0.125 | 0.901 | -2.07e+05 | 1.82e+05 |
| industry: accessibility | -9.56e+04 | 9.92e+04 | -0.964 | 0.335 | -2.9e+05 | 9.88e+04 |
| industry: Accessibility | -862 | 9.92e+04 | -0.009 | 0.993 | -1.95e+05 | 1.94e+05 |
| industry: Accounting, Banking & Finance | 5.8e+03 | 7.01e+04 | 0.083 | 0.934 | -1.32e+05 | 1.43e+05 |
| industry: Administration | -4.66e+04 | 8.59e+04 | -0.542 | 0.588 | -2.15e+05 | 1.22e+05 |
| industry: Administration (food service) | -5.55e+04 | 9.91e+04 | -0.56 | 0.576 | -2.5e+05 | 1.39e+05 |
| industry: Administrative | -2.23e+04 | 9.92e+04 | -0.225 | 0.822 | -2.17e+05 | 1.72e+05 |
| industry: Administrative Support | 97.2 | 9.92e+04 | 0.001 | 0.999 | -1.94e+05 | 1.94e+05 |
| industry: Administrative Work | -2.39e+04 | 9.92e+04 | -0.241 | 0.81 | -2.18e+05 | 1.71e+05 |
| industry: Aerospace | 1.67e+04 | 7.84e+04 | 0.214 | 0.831 | -1.37e+05 | 1.7e+05 |
| industry: Aerospace & Defense | 4.34e+04 | 9.91e+04 | 0.437 | 0.662 | -1.51e+05 | 2.38e+05 |
| industry: Aerospace and Defense | 3.1e+03 | 8.59e+04 | 0.036 | 0.971 | -1.65e+05 | 1.71e+05 |
| industry: Aerospace and Defense Manufacturing | -3.33e+04 | 9.92e+04 | -0.336 | 0.737 | -2.28e+05 | 1.61e+05 |
| industry: Aerospace and Defense/Government Contracting | -1.33e+04 | 9.91e+04 | -0.134 | 0.894 | -2.08e+05 | 1.81e+05 |
| industry: Aerospace contracting | 2.41e+04 | 9.92e+04 | 0.243 | 0.808 | -1.7e+05 | 2.18e+05 |
| industry: aerospace data | -2.15e+04 | 9.92e+04 | -0.217 | 0.828 | -2.16e+05 | 1.73e+05 |
| industry: Aerospace/Aviation | 2.17e+03 | 9.92e+04 | 0.022 | 0.983 | -1.92e+05 | 1.97e+05 |
| industry: Aerospace/Defense | 1.3e+04 | 9.92e+04 | 0.131 | 0.896 | -1.81e+05 | 2.07e+05 |
| industry: Affordable Housing Real Estate Development (nonprofit) | 3.33e+04 | 9.92e+04 | 0.336 | 0.737 | -1.61e+05 | 2.28e+05 |
| industry: Agriculture or Forestry | -8.92e+03 | 7.05e+04 | -0.127 | 0.899 | -1.47e+05 | 1.29e+05 |
| industry: Agriculture/Agriculture Chemical | -1.52e+03 | 9.92e+04 | -0.015 | 0.988 | -1.96e+05 | 1.93e+05 |
| industry: Airline | -4.9e+03 | 9.92e+04 | -0.049 | 0.961 | -1.99e+05 | 1.89e+05 |
| industry: AmeriCorps | -6.31e+04 | 9.92e+04 | -0.636 | 0.525 | -2.57e+05 | 1.31e+05 |
| industry: Analytical Chemistry | -4.68e+04 | 9.92e+04 | -0.472 | 0.637 | -2.41e+05 | 1.48e+05 |
| industry: Analytical lab | -4.45e+04 | 9.92e+04 | -0.449 | 0.654 | -2.39e+05 | 1.5e+05 |
| industry: Analytics | 5.15e+04 | 9.92e+04 | 0.52 | 0.603 | -1.43e+05 | 2.46e+05 |
| industry: Animal care | -3.28e+04 | 9.91e+04 | -0.331 | 0.741 | -2.27e+05 | 1.62e+05 |
| industry: Animal Care | -6.13e+04 | 9.92e+04 | -0.618 | 0.536 | -2.56e+05 | 1.33e+05 |
| industry: Animal Caretaker | -1.4e+04 | 1.02e+05 | -0.137 | 0.891 | -2.14e+05 | 1.86e+05 |
| industry: Animal Health | -1.29e+04 | 9.92e+04 | -0.13 | 0.896 | -2.07e+05 | 1.81e+05 |
| industry: Animal health industry | 4.01e+04 | 9.92e+04 | 0.404 | 0.686 | -1.54e+05 | 2.35e+05 |
| industry: Animal Health Product Manufacturing | -9.64e+03 | 9.91e+04 | -0.097 | 0.923 | -2.04e+05 | 1.85e+05 |
| industry: Animal welfare | -1.73e+03 | 9.91e+04 | -0.017 | 0.986 | -1.96e+05 | 1.93e+05 |
| industry: Apparel | 6.44e+03 | 8.1e+04 | 0.08 | 0.937 | -1.52e+05 | 1.65e+05 |
| industry: apparel design/product development | -1.3e+04 | 9.91e+04 | -0.131 | 0.896 | -2.07e+05 | 1.81e+05 |
| industry: Apparel manufacture | -3.37e+04 | 9.96e+04 | -0.339 | 0.735 | -2.29e+05 | 1.61e+05 |
| industry: Archaeology | -4.29e+04 | 9.92e+04 | -0.432 | 0.666 | -2.37e+05 | 1.51e+05 |
| industry: Archaeology / Cultural Resource Management | -1.89e+04 | 9.92e+04 | -0.19 | 0.849 | -2.13e+05 | 1.75e+05 |
| industry: Archaeology/Cultural Resource Manager | 1.27e+03 | 9.93e+04 | 0.013 | 0.99 | -1.93e+05 | 1.96e+05 |
| industry: Architect | 1.06e+04 | 8.59e+04 | 0.124 | 0.902 | -1.58e+05 | 1.79e+05 |
| industry: Architectural/Land Planning/Civil Engineering | -3.73e+04 | 9.92e+04 | -0.376 | 0.707 | -2.32e+05 | 1.57e+05 |
| industry: architecture | -4.86e+03 | 9.92e+04 | -0.049 | 0.961 | -1.99e+05 | 1.9e+05 |
| industry: Architecture | -1.3e+04 | 7.16e+04 | -0.181 | 0.856 | -1.53e+05 | 1.27e+05 |
| industry: Architecture / Engineering | -2.79e+04 | 9.92e+04 | -0.281 | 0.778 | -2.22e+05 | 1.66e+05 |
| industry: Architecture & Construction | -2.9e+04 | 9.92e+04 | -0.292 | 0.77 | -2.23e+05 | 1.65e+05 |
| industry: Architecture and engineering consulting and design | -2.99e+03 | 9.92e+04 | -0.03 | 0.976 | -1.97e+05 | 1.91e+05 |
| industry: Architecture, Engineering, Construction | 2e+04 | 9.92e+04 | 0.202 | 0.84 | -1.74e+05 | 2.15e+05 |
| industry: Architecture/Construction | -1.1e+03 | 9.92e+04 | -0.011 | 0.991 | -1.95e+05 | 1.93e+05 |
| industry: Archives | -3.1e+04 | 8.1e+04 | -0.383 | 0.702 | -1.9e+05 | 1.28e+05 |
| industry: Archives/Libraries | -1.62e+04 | 9.92e+04 | -0.164 | 0.87 | -2.11e+05 | 1.78e+05 |
| industry: Archives/Library Science | -2.57e+04 | 9.91e+04 | -0.26 | 0.795 | -2.2e+05 | 1.69e+05 |
| industry: Art & Design | 3.78e+03 | 7.02e+04 | 0.054 | 0.957 | -1.34e+05 | 1.41e+05 |
| industry: art appraisal | -3.59e+04 | 9.92e+04 | -0.362 | 0.718 | -2.3e+05 | 1.59e+05 |
| industry: Arts Administration | -2.36e+04 | 9.91e+04 | -0.238 | 0.812 | -2.18e+05 | 1.71e+05 |
| industry: Association | 1.12e+04 | 9.92e+04 | 0.112 | 0.91 | -1.83e+05 | 2.06e+05 |
| industry: association management | -2.01e+04 | 9.92e+04 | -0.203 | 0.839 | -2.14e+05 | 1.74e+05 |
| industry: Association Management | -2.93e+04 | 9.92e+04 | -0.295 | 0.768 | -2.24e+05 | 1.65e+05 |
| industry: Auction house | -1.99e+04 | 9.92e+04 | -0.2 | 0.841 | -2.14e+05 | 1.75e+05 |
| industry: Auction House | -4.98e+04 | 9.92e+04 | -0.502 | 0.616 | -2.44e+05 | 1.45e+05 |
| industry: Auto Mfg. | 9.83e+03 | 9.92e+04 | 0.099 | 0.921 | -1.85e+05 | 2.04e+05 |
| industry: auto repair | -4.36e+04 | 8.59e+04 | -0.508 | 0.611 | -2.12e+05 | 1.25e+05 |
| industry: Auto Repair | -4.42e+04 | 9.92e+04 | -0.445 | 0.656 | -2.39e+05 | 1.5e+05 |
| industry: Automotive | 1.61e+04 | 7.44e+04 | 0.216 | 0.829 | -1.3e+05 | 1.62e+05 |
| industry: Automotive finance and insurance | 2.86e+03 | 9.91e+04 | 0.029 | 0.977 | -1.91e+05 | 1.97e+05 |
| industry: Automotive repair | -6.67e+04 | 9.92e+04 | -0.672 | 0.501 | -2.61e+05 | 1.28e+05 |
| industry: Automotive Repair | -2.82e+04 | 9.92e+04 | -0.284 | 0.776 | -2.23e+05 | 1.66e+05 |
| industry: Automotive technician | -4.16e+04 | 9.92e+04 | -0.419 | 0.675 | -2.36e+05 | 1.53e+05 |
| industry: Automtive Repair | -2.55e+04 | 9.93e+04 | -0.257 | 0.797 | -2.2e+05 | 1.69e+05 |
| industry: aviation | -3.15e+04 | 9.92e+04 | -0.318 | 0.75 | -2.26e+05 | 1.63e+05 |
| industry: Aviation | -4.32e+04 | 9.92e+04 | -0.435 | 0.664 | -2.38e+05 | 1.51e+05 |
| industry: awards and engraving | -3.75e+04 | 9.92e+04 | -0.378 | 0.705 | -2.32e+05 | 1.57e+05 |
| industry: B2B Services | 215 | 1.02e+05 | 0.002 | 0.998 | -2e+05 | 2.01e+05 |
| industry: Background Screening | -1.56e+04 | 9.91e+04 | -0.158 | 0.875 | -2.1e+05 | 1.79e+05 |
| industry: Beauty | -1e+04 | 9.92e+04 | -0.101 | 0.919 | -2.04e+05 | 1.84e+05 |
| industry: Beauty /CPG | 3.38e+04 | 9.92e+04 | 0.34 | 0.734 | -1.61e+05 | 2.28e+05 |
| industry: Beauty Manufacturing & Education | -3.95e+04 | 9.92e+04 | -0.398 | 0.69 | -2.34e+05 | 1.55e+05 |
| industry: Beauty, Cosmetics, Fragrance | 2.43e+04 | 9.92e+04 | 0.244 | 0.807 | -1.7e+05 | 2.19e+05 |
| industry: Beauty/service industry | -779 | 9.92e+04 | -0.008 | 0.994 | -1.95e+05 | 1.94e+05 |
| industry: Beer sales | -4.88e+04 | 9.92e+04 | -0.492 | 0.623 | -2.43e+05 | 1.46e+05 |
| industry: Behavior analysis/mental health | 1.15e+04 | 9.92e+04 | 0.116 | 0.908 | -1.83e+05 | 2.06e+05 |
| industry: Behavioral Health | -1.12e+05 | 9.93e+04 | -1.13 | 0.258 | -3.07e+05 | 8.24e+04 |
| industry: Benefits Administration | 4.84e+03 | 8.59e+04 | 0.056 | 0.955 | -1.63e+05 | 1.73e+05 |
| industry: Beverage | -1.15e+04 | 8.59e+04 | -0.134 | 0.894 | -1.8e+05 | 1.57e+05 |
| industry: Beverage & Spirits | 5.13e+03 | 9.92e+04 | 0.052 | 0.959 | -1.89e+05 | 1.99e+05 |
| industry: Beverage Distribution | -2.75e+04 | 9.92e+04 | -0.277 | 0.782 | -2.22e+05 | 1.67e+05 |
| industry: Beverage Production | -5.01e+04 | 9.92e+04 | -0.505 | 0.614 | -2.44e+05 | 1.44e+05 |
| industry: Big Pharma | 9.3e+04 | 9.92e+04 | 0.937 | 0.349 | -1.01e+05 | 2.87e+05 |
| industry: Bio tech | 2.07e+04 | 9.95e+04 | 0.208 | 0.835 | -1.74e+05 | 2.16e+05 |
| industry: Bioinformatics | 1.21e+04 | 9.92e+04 | 0.122 | 0.903 | -1.82e+05 | 2.06e+05 |
| industry: biological research | -2.18e+04 | 9.92e+04 | -0.22 | 0.826 | -2.16e+05 | 1.73e+05 |
| industry: Biological Sciences | 2.22e+04 | 9.92e+04 | 0.223 | 0.823 | -1.72e+05 | 2.17e+05 |
| industry: Biology/Research | -5.92e+04 | 9.93e+04 | -0.596 | 0.551 | -2.54e+05 | 1.35e+05 |
| industry: biomedical research | -2.83e+04 | 8.59e+04 | -0.329 | 0.742 | -1.97e+05 | 1.4e+05 |
| industry: Biomedical research | -1.93e+04 | 8.59e+04 | -0.224 | 0.823 | -1.88e+05 | 1.49e+05 |
| industry: Biomedical Research | -2.58e+04 | 7.44e+04 | -0.347 | 0.729 | -1.72e+05 | 1.2e+05 |
| industry: Biopharma | 3.94e+04 | 7.84e+04 | 0.503 | 0.615 | -1.14e+05 | 1.93e+05 |
| industry: Biopharmaceuticals | 5.85e+04 | 9.92e+04 | 0.59 | 0.555 | -1.36e+05 | 2.53e+05 |
| industry: Bioscience Company | -3.71e+04 | 9.91e+04 | -0.375 | 0.708 | -2.31e+05 | 1.57e+05 |
| industry: biotech | 1.19e+04 | 9.92e+04 | 0.12 | 0.905 | -1.83e+05 | 2.06e+05 |
| industry: Biotech | 1.79e+04 | 7.09e+04 | 0.252 | 0.801 | -1.21e+05 | 1.57e+05 |
| industry: BioTech | -1.06e+04 | 9.91e+04 | -0.107 | 0.915 | -2.05e+05 | 1.84e+05 |
| industry: Biotech / life sciences | 1.05e+05 | 9.92e+04 | 1.06 | 0.289 | -8.91e+04 | 3e+05 |
| industry: Biotech / Pharmaceutical Industry | 7.21e+04 | 9.92e+04 | 0.727 | 0.468 | -1.22e+05 | 2.67e+05 |
| industry: Biotech / Research | -6.13e+03 | 9.92e+04 | -0.062 | 0.951 | -2.01e+05 | 1.88e+05 |
| industry: Biotech manufacturing | -1.31e+03 | 9.92e+04 | -0.013 | 0.989 | -1.96e+05 | 1.93e+05 |
| industry: Biotech research | 1.41e+04 | 9.92e+04 | 0.143 | 0.887 | -1.8e+05 | 2.08e+05 |
| industry: Biotech Research | -4.94e+04 | 9.92e+04 | -0.498 | 0.619 | -2.44e+05 | 1.45e+05 |
| industry: Biotech/Drug Development | 1.51e+04 | 8.59e+04 | 0.176 | 0.86 | -1.53e+05 | 1.83e+05 |
| industry: Biotech/Food Safety | -3.69e+04 | 9.92e+04 | -0.372 | 0.71 | -2.31e+05 | 1.58e+05 |
| industry: Biotech/pharma | 2.58e+04 | 8.59e+04 | 0.3 | 0.764 | -1.43e+05 | 1.94e+05 |
| industry: Biotech/Pharma | 3.46e+04 | 7.39e+04 | 0.468 | 0.64 | -1.1e+05 | 1.79e+05 |
| industry: Biotech/pharmaceuticals | 1.06e+05 | 8.1e+04 | 1.3 | 0.193 | -5.32e+04 | 2.64e+05 |
| industry: Biotech/Pharmaceuticals | -2.11e+03 | 9.92e+04 | -0.021 | 0.983 | -1.97e+05 | 1.92e+05 |
| industry: Biotech/software | 3.03e+04 | 9.92e+04 | 0.306 | 0.76 | -1.64e+05 | 2.25e+05 |
| industry: biotechnology | 4.74e+04 | 9.95e+04 | 0.477 | 0.633 | -1.47e+05 | 2.42e+05 |
| industry: Biotechnology | 1.57e+04 | 7.21e+04 | 0.218 | 0.828 | -1.26e+05 | 1.57e+05 |
| industry: Biotechnology/Life Sciences | 5.5e+03 | 9.92e+04 | 0.056 | 0.956 | -1.89e+05 | 2e+05 |
| industry: Bitech | 9.04e+04 | 9.91e+04 | 0.911 | 0.362 | -1.04e+05 | 2.85e+05 |
| industry: Book publishing | -3.07e+04 | 8.59e+04 | -0.357 | 0.721 | -1.99e+05 | 1.38e+05 |
| industry: Book Publishing | -2.99e+04 | 7.84e+04 | -0.382 | 0.703 | -1.84e+05 | 1.24e+05 |
| industry: Brain research | -5.69e+04 | 9.92e+04 | -0.574 | 0.566 | -2.51e+05 | 1.37e+05 |
| industry: Brewing | -3.59e+04 | 9.92e+04 | -0.362 | 0.717 | -2.3e+05 | 1.58e+05 |
| industry: Business or Consulting | 1.96e+04 | 7.02e+04 | 0.279 | 0.78 | -1.18e+05 | 1.57e+05 |
| industry: Business Process Outsourcing | -1.54e+04 | 9.92e+04 | -0.155 | 0.877 | -2.1e+05 | 1.79e+05 |
| industry: Business Services | -5.02e+04 | 9.92e+04 | -0.506 | 0.613 | -2.45e+05 | 1.44e+05 |
| industry: Buyer | 9.19e+03 | 9.92e+04 | 0.093 | 0.926 | -1.85e+05 | 2.04e+05 |
| industry: Call center | -3.27e+04 | 9.92e+04 | -0.33 | 0.742 | -2.27e+05 | 1.62e+05 |
| industry: CALL CENTER | -5.46e+04 | 9.92e+04 | -0.55 | 0.582 | -2.49e+05 | 1.4e+05 |
| industry: Cancer research, not for profit | -1.39e+04 | 9.92e+04 | -0.14 | 0.889 | -2.08e+05 | 1.81e+05 |
| industry: Cannabis | -1.84e+04 | 7.84e+04 | -0.234 | 0.815 | -1.72e+05 | 1.35e+05 |
| industry: Cannabis compliance | 1.87e+03 | 9.92e+04 | 0.019 | 0.985 | -1.93e+05 | 1.96e+05 |
| industry: Career & Technical Training | -4.85e+04 | 9.92e+04 | -0.489 | 0.625 | -2.43e+05 | 1.46e+05 |
| industry: Caregiver | -5.16e+04 | 9.92e+04 | -0.521 | 0.603 | -2.46e+05 | 1.43e+05 |
| industry: CBD Manufacturing | -5.27e+03 | 9.91e+04 | -0.053 | 0.958 | -2e+05 | 1.89e+05 |
| industry: Chaplain | -1.03e+05 | 9.92e+04 | -1.04 | 0.299 | -2.97e+05 | 9.15e+04 |
| industry: Chemical Manufacturing | -2.69e+04 | 1e+05 | -0.269 | 0.788 | -2.23e+05 | 1.69e+05 |
| industry: Chemicals | 1.76e+03 | 9.92e+04 | 0.018 | 0.986 | -1.93e+05 | 1.96e+05 |
| industry: Chemicals/ Materials | 7.87e+03 | 9.92e+04 | 0.079 | 0.937 | -1.87e+05 | 2.02e+05 |
| industry: chemistry | -2.52e+04 | 9.92e+04 | -0.255 | 0.799 | -2.2e+05 | 1.69e+05 |
| industry: Chemistry | -8.18e+03 | 8.59e+04 | -0.095 | 0.924 | -1.77e+05 | 1.6e+05 |
| industry: Child care | -3.68e+04 | 8.59e+04 | -0.429 | 0.668 | -2.05e+05 | 1.32e+05 |
| industry: Child Care Resource and Referral Agency | -7.87e+04 | 9.92e+04 | -0.793 | 0.428 | -2.73e+05 | 1.16e+05 |
| industry: Childcare | -2.06e+04 | 7.84e+04 | -0.262 | 0.793 | -1.74e+05 | 1.33e+05 |
| industry: Children's Book Wholesale | -4.29e+04 | 9.92e+04 | -0.432 | 0.666 | -2.37e+05 | 1.51e+05 |
| industry: Church | -3.01e+04 | 8.59e+04 | -0.35 | 0.726 | -1.98e+05 | 1.38e+05 |
| industry: Church ministry | -6.1e+04 | 9.92e+04 | -0.615 | 0.539 | -2.55e+05 | 1.33e+05 |
| industry: Clean Energy (eg. energy efficiency, renewables, etc.) | 9.39e+04 | 9.92e+04 | 0.946 | 0.344 | -1.01e+05 | 2.88e+05 |
| industry: Clergy | -3.01e+04 | 7.84e+04 | -0.384 | 0.701 | -1.84e+05 | 1.24e+05 |
| industry: Clinical & Translational Reserach | -1.73e+04 | 9.92e+04 | -0.175 | 0.861 | -2.12e+05 | 1.77e+05 |
| industry: clinical research | -1.05e+04 | 8.59e+04 | -0.122 | 0.903 | -1.79e+05 | 1.58e+05 |
| industry: Clinical research | -1.56e+04 | 8.59e+04 | -0.182 | 0.856 | -1.84e+05 | 1.53e+05 |
| industry: Clinical Research | 5.08e+03 | 8.14e+04 | 0.062 | 0.95 | -1.54e+05 | 1.65e+05 |
| industry: clinical trials research coordination | -6.65e+04 | 9.92e+04 | -0.67 | 0.503 | -2.61e+05 | 1.28e+05 |
| industry: coaching | -6e+04 | 9.92e+04 | -0.605 | 0.545 | -2.54e+05 | 1.34e+05 |
| industry: Coffee - Importing | 1.09e+04 | 9.96e+04 | 0.109 | 0.913 | -1.84e+05 | 2.06e+05 |
| industry: College Athletics | -1.83e+04 | 9.92e+04 | -0.185 | 0.853 | -2.13e+05 | 1.76e+05 |
| industry: Commercial Building Material Distribution | 3.02e+05 | 9.92e+04 | 3.04 | 0.002 | 1.07e+05 | 4.96e+05 |
| industry: Commercial Fisherman | -4.97e+04 | 9.92e+04 | -0.501 | 0.616 | -2.44e+05 | 1.45e+05 |
| industry: Commercial furniture | -4.23e+03 | 9.92e+04 | -0.043 | 0.966 | -1.99e+05 | 1.9e+05 |
| industry: Commercial Landscaping | -6.37e+04 | 9.92e+04 | -0.642 | 0.521 | -2.58e+05 | 1.31e+05 |
| industry: Commercial Real Estate | 5.26e+03 | 7.5e+04 | 0.07 | 0.944 | -1.42e+05 | 1.52e+05 |
| industry: Commercial Real Estate - Private Equity | -6.5e+03 | 9.92e+04 | -0.066 | 0.948 | -2.01e+05 | 1.88e+05 |
| industry: Commercial Real Estate Data and Analytics/Research | -1.59e+04 | 9.91e+04 | -0.161 | 0.872 | -2.1e+05 | 1.78e+05 |
| industry: commodities trading | -2.78e+04 | 9.92e+04 | -0.28 | 0.779 | -2.22e+05 | 1.67e+05 |
| industry: Communication Research | -2.77e+04 | 9.92e+04 | -0.279 | 0.78 | -2.22e+05 | 1.67e+05 |
| industry: Communications | 1.75e+03 | 8.1e+04 | 0.022 | 0.983 | -1.57e+05 | 1.6e+05 |
| industry: Community Foundation | 5.5e+03 | 9.92e+04 | 0.056 | 0.956 | -1.89e+05 | 2e+05 |
| industry: Community Management | -1.21e+04 | 9.92e+04 | -0.122 | 0.903 | -2.07e+05 | 1.82e+05 |
| industry: Compliance | -3.38e+04 | 8.1e+04 | -0.417 | 0.676 | -1.93e+05 | 1.25e+05 |
| industry: Computing or Tech | 4.01e+04 | 7.01e+04 | 0.572 | 0.567 | -9.73e+04 | 1.78e+05 |
| industry: Concrete | -4.06e+04 | 9.91e+04 | -0.41 | 0.682 | -2.35e+05 | 1.54e+05 |
| industry: Construction | -4.29e+03 | 7.36e+04 | -0.058 | 0.953 | -1.48e+05 | 1.4e+05 |
| industry: Construction Management | -6.4e+03 | 9.93e+04 | -0.064 | 0.949 | -2.01e+05 | 1.88e+05 |
| industry: Construction, HVAC | -1.22e+04 | 9.91e+04 | -0.123 | 0.902 | -2.07e+05 | 1.82e+05 |
| industry: Consultant | -4.27e+04 | 9.92e+04 | -0.43 | 0.667 | -2.37e+05 | 1.52e+05 |
| industry: consulting | 1.07e+05 | 9.92e+04 | 1.08 | 0.28 | -8.72e+04 | 3.01e+05 |
| industry: Consulting | 8.01e+03 | 7.68e+04 | 0.104 | 0.917 | -1.43e+05 | 1.59e+05 |
| industry: Consulting / Professional Services | -1.02e+04 | 9.92e+04 | -0.103 | 0.918 | -2.05e+05 | 1.84e+05 |
| industry: Consulting Operations- Big 4 | 1.51e+04 | 9.92e+04 | 0.152 | 0.879 | -1.79e+05 | 2.09e+05 |
| industry: Consumer goods | 6.26e+04 | 8.59e+04 | 0.729 | 0.466 | -1.06e+05 | 2.31e+05 |
| industry: Consumer Goods | -4.26e+03 | 8.1e+04 | -0.053 | 0.958 | -1.63e+05 | 1.54e+05 |
| industry: Consumer Goods Production | -1.03e+04 | 9.91e+04 | -0.104 | 0.918 | -2.05e+05 | 1.84e+05 |
| industry: Consumer packaged goods | 2.01e+04 | 9.92e+04 | 0.203 | 0.839 | -1.74e+05 | 2.15e+05 |
| industry: Consumer Packaged Goods | 1.82e+04 | 7.84e+04 | 0.232 | 0.817 | -1.35e+05 | 1.72e+05 |
| industry: consumer product design | 1.09e+03 | 9.92e+04 | 0.011 | 0.991 | -1.93e+05 | 1.95e+05 |
| industry: Consumer Product Goods | 9.33e+03 | 9.92e+04 | 0.094 | 0.925 | -1.85e+05 | 2.04e+05 |
| industry: Consumer Product Organization | -1.31e+04 | 9.92e+04 | -0.132 | 0.895 | -2.08e+05 | 1.81e+05 |
| industry: consumer products | 7.34e+04 | 9.92e+04 | 0.74 | 0.459 | -1.21e+05 | 2.68e+05 |
| industry: Consumer Products design | -1.56e+04 | 9.91e+04 | -0.158 | 0.875 | -2.1e+05 | 1.79e+05 |
| industry: Consumer Research | -1.41e+04 | 9.92e+04 | -0.142 | 0.887 | -2.09e+05 | 1.8e+05 |
| industry: Consumer/Packaged Goods | 2.97e+04 | 9.91e+04 | 0.3 | 0.764 | -1.65e+05 | 2.24e+05 |
| industry: Content Review - Copyright/DMCA | -2.24e+04 | 1.05e+05 | -0.213 | 0.831 | -2.29e+05 | 1.84e+05 |
| industry: Contract Research | 1.29e+04 | 9.92e+04 | 0.13 | 0.897 | -1.82e+05 | 2.07e+05 |
| industry: Corporate accounting in death care (funeral & cemetery) | 5.13e+03 | 9.92e+04 | 0.052 | 0.959 | -1.89e+05 | 1.99e+05 |
| industry: Corporate Learning and Development | -2.4e+04 | 9.92e+04 | -0.242 | 0.809 | -2.18e+05 | 1.7e+05 |
| industry: Corporate sustainability | 3.02e+04 | 9.92e+04 | 0.304 | 0.761 | -1.64e+05 | 2.25e+05 |
| industry: Corporate Sustainability | 3.15e+04 | 9.92e+04 | 0.318 | 0.751 | -1.63e+05 | 2.26e+05 |
| industry: Corporate Training | 1.76e+05 | 9.92e+04 | 1.77 | 0.076 | -1.84e+04 | 3.7e+05 |
| industry: Corporate Travel Industry | -5.31e+03 | 9.92e+04 | -0.054 | 0.957 | -2e+05 | 1.89e+05 |
| industry: Cosmetology | 862 | 9.92e+04 | 0.009 | 0.993 | -1.94e+05 | 1.95e+05 |
| industry: Counseling | -3.85e+04 | 9.92e+04 | -0.388 | 0.698 | -2.33e+05 | 1.56e+05 |
| industry: CPG / Retail | 9.65e+04 | 9.92e+04 | 0.973 | 0.33 | -9.78e+04 | 2.91e+05 |
| industry: Craft Beer Industry | -1.46e+04 | 9.91e+04 | -0.148 | 0.883 | -2.09e+05 | 1.8e+05 |
| industry: Cultural Heritage | -2.76e+04 | 9.92e+04 | -0.278 | 0.781 | -2.22e+05 | 1.67e+05 |
| industry: Cultural Resource Management | -3.44e+04 | 9.92e+04 | -0.347 | 0.729 | -2.29e+05 | 1.6e+05 |
| industry: Cultural Resources Management/Major Univ. | -6.79e+04 | 9.92e+04 | -0.684 | 0.494 | -2.62e+05 | 1.26e+05 |
| industry: Customer service | -2.35e+04 | 9.92e+04 | -0.237 | 0.813 | -2.18e+05 | 1.71e+05 |
| industry: Customer Service | -3.26e+04 | 8.1e+04 | -0.403 | 0.687 | -1.91e+05 | 1.26e+05 |
| industry: Customer service/call center | -4.64e+04 | 9.92e+04 | -0.468 | 0.64 | -2.41e+05 | 1.48e+05 |
| industry: Customer service/publishing-adjacent | -3.68e+04 | 9.92e+04 | -0.371 | 0.711 | -2.31e+05 | 1.58e+05 |
| industry: Data Analytics | -1.21e+04 | 9.92e+04 | -0.122 | 0.903 | -2.07e+05 | 1.82e+05 |
| industry: Data Breach | -1.93e+04 | 9.92e+04 | -0.195 | 0.846 | -2.14e+05 | 1.75e+05 |
| industry: Data Entry | -2.67e+04 | 9.92e+04 | -0.269 | 0.788 | -2.21e+05 | 1.68e+05 |
| industry: Data/Institutional Research in Higher Education | -1.77e+04 | 9.92e+04 | -0.178 | 0.859 | -2.12e+05 | 1.77e+05 |
| industry: Database subscription services | 3.06e+04 | 9.95e+04 | 0.308 | 0.758 | -1.64e+05 | 2.26e+05 |
| industry: Defense | 2.67e+04 | 7.57e+04 | 0.353 | 0.724 | -1.22e+05 | 1.75e+05 |
| industry: Defense contracting | 2.19e+03 | 9.92e+04 | 0.022 | 0.982 | -1.92e+05 | 1.97e+05 |
| industry: Defense Contracting | 2.36e+04 | 8.59e+04 | 0.274 | 0.784 | -1.45e+05 | 1.92e+05 |
| industry: Defense Contractor | -1.88e+04 | 9.92e+04 | -0.19 | 0.849 | -2.13e+05 | 1.76e+05 |
| industry: Delivery and installation for commercial machinery | -5.55e+04 | 9.92e+04 | -0.56 | 0.576 | -2.5e+05 | 1.39e+05 |
| industry: Diagnostic Medical Devices | -3.08e+04 | 9.93e+04 | -0.31 | 0.756 | -2.25e+05 | 1.64e+05 |
| industry: Digital Marketing | -7.94e+03 | 9.91e+04 | -0.08 | 0.936 | -2.02e+05 | 1.86e+05 |
| industry: digital marketing within a book publishing company (please reclassify as you see fit) | -3.49e+04 | 9.92e+04 | -0.352 | 0.725 | -2.29e+05 | 1.59e+05 |
| industry: Direct support professional in a group home | -8.85e+04 | 9.92e+04 | -0.893 | 0.372 | -2.83e+05 | 1.06e+05 |
| industry: Distribution | 1.56e+04 | 8.61e+04 | 0.181 | 0.856 | -1.53e+05 | 1.84e+05 |
| industry: Diversity, Equity & Inclusion | 5.78e+04 | 9.97e+04 | 0.579 | 0.562 | -1.38e+05 | 2.53e+05 |
| industry: DoD Contracting | 8.1e+03 | 9.92e+04 | 0.082 | 0.935 | -1.86e+05 | 2.02e+05 |
| industry: Drug development | 2.5e+04 | 9.92e+04 | 0.252 | 0.801 | -1.69e+05 | 2.19e+05 |
| industry: E commerce | -4.26e+04 | 9.91e+04 | -0.43 | 0.667 | -2.37e+05 | 1.52e+05 |
| industry: e-comm | 1.74e+04 | 9.92e+04 | 0.175 | 0.861 | -1.77e+05 | 2.12e+05 |
| industry: E-commerce | -3e+04 | 8.1e+04 | -0.37 | 0.712 | -1.89e+05 | 1.29e+05 |
| industry: E-Commerce | -2.03e+04 | 9.92e+04 | -0.204 | 0.838 | -2.15e+05 | 1.74e+05 |
| industry: e-learning | -2.21e+04 | 9.92e+04 | -0.223 | 0.824 | -2.16e+05 | 1.72e+05 |
| industry: Eap | -1.6e+03 | 9.91e+04 | -0.016 | 0.987 | -1.96e+05 | 1.93e+05 |
| industry: Early childhood education | -5.13e+04 | 9.92e+04 | -0.517 | 0.605 | -2.46e+05 | 1.43e+05 |
| industry: Early Childhood Education | -1.85e+04 | 8.59e+04 | -0.215 | 0.83 | -1.87e+05 | 1.5e+05 |
| industry: Early childhood education (preschool) | -6.52e+03 | 9.97e+04 | -0.065 | 0.948 | -2.02e+05 | 1.89e+05 |
| industry: Early Education (corporate office) | -2.29e+04 | 9.92e+04 | -0.231 | 0.818 | -2.17e+05 | 1.72e+05 |
| industry: Earth sciences | 5.92e+03 | 9.92e+04 | 0.06 | 0.952 | -1.88e+05 | 2e+05 |
| industry: Ecology | -2.45e+04 | 9.92e+04 | -0.247 | 0.805 | -2.19e+05 | 1.7e+05 |
| industry: Ecommerce | -3.93e+04 | 1e+05 | -0.393 | 0.694 | -2.35e+05 | 1.57e+05 |
| industry: ECommerce | 3.54e+04 | 9.91e+04 | 0.357 | 0.721 | -1.59e+05 | 2.3e+05 |
| industry: Ecommerce - Technology | 3.45e+04 | 9.92e+04 | 0.348 | 0.728 | -1.6e+05 | 2.29e+05 |
| industry: Ecommerce fraud | 2.67e+04 | 9.92e+04 | 0.269 | 0.788 | -1.68e+05 | 2.21e+05 |
| industry: Ed Tech | 2.47e+04 | 9.91e+04 | 0.249 | 0.803 | -1.7e+05 | 2.19e+05 |
| industry: Editor in educational publishing | -3.1e+04 | 9.92e+04 | -0.313 | 0.754 | -2.26e+05 | 1.63e+05 |
| industry: EdTech | 2.47e+04 | 9.91e+04 | 0.249 | 0.803 | -1.7e+05 | 2.19e+05 |
| industry: Educ tech | -4.36e+04 | 9.92e+04 | -0.44 | 0.66 | -2.38e+05 | 1.51e+05 |
| industry: Education (Higher Education) | -2.67e+04 | 7.01e+04 | -0.381 | 0.703 | -1.64e+05 | 1.11e+05 |
| industry: Education (Primary/Secondary) | -2.64e+04 | 7.02e+04 | -0.376 | 0.707 | -1.64e+05 | 1.11e+05 |
| industry: Education Consulting | -2.49e+04 | 9.92e+04 | -0.252 | 0.801 | -2.19e+05 | 1.69e+05 |
| industry: Education Publishing | -1.25e+04 | 9.91e+04 | -0.126 | 0.9 | -2.07e+05 | 1.82e+05 |
| industry: Education research- mix of Edtech and non profits | 2.89e+04 | 9.92e+04 | 0.291 | 0.771 | -1.66e+05 | 2.23e+05 |
| industry: Education service provider | 1.81e+04 | 9.92e+04 | 0.183 | 0.855 | -1.76e+05 | 2.12e+05 |
| industry: Education services (tutoring) | -1.59e+04 | 9.92e+04 | -0.16 | 0.873 | -2.1e+05 | 1.78e+05 |
| industry: Education start-up | 2.12e+04 | 9.92e+04 | 0.213 | 0.831 | -1.73e+05 | 2.16e+05 |
| industry: education writing | -7.35e+03 | 9.92e+04 | -0.074 | 0.941 | -2.02e+05 | 1.87e+05 |
| industry: Education- museum/public outreach | -2.25e+04 | 9.92e+04 | -0.227 | 0.82 | -2.17e+05 | 1.72e+05 |
| industry: Education: preschool | -4.29e+04 | 9.92e+04 | -0.433 | 0.665 | -2.37e+05 | 1.51e+05 |
| industry: Education/vocational | -8.8e+03 | 9.92e+04 | -0.089 | 0.929 | -2.03e+05 | 1.86e+05 |
| industry: Educational assessment | -2.39e+04 | 9.92e+04 | -0.241 | 0.81 | -2.18e+05 | 1.7e+05 |
| industry: Educational products | -2.71e+04 | 9.92e+04 | -0.273 | 0.785 | -2.21e+05 | 1.67e+05 |
| industry: educational publishing | -1.58e+04 | 9.92e+04 | -0.159 | 0.874 | -2.1e+05 | 1.79e+05 |
| industry: Educational Publishing | 4.5e+03 | 9.92e+04 | 0.045 | 0.964 | -1.9e+05 | 1.99e+05 |
| industry: Educational publishing / ed tech | -1.49e+04 | 9.92e+04 | -0.151 | 0.88 | -2.09e+05 | 1.79e+05 |
| industry: Educational Research | -2.86e+04 | 9.91e+04 | -0.289 | 0.773 | -2.23e+05 | 1.66e+05 |
| industry: Educational technology | -1.91e+04 | 8.59e+04 | -0.223 | 0.824 | -1.88e+05 | 1.49e+05 |
| industry: Educational Technology - hybrid between book publishing and technology really | -7.86e+03 | 9.92e+04 | -0.079 | 0.937 | -2.02e+05 | 1.87e+05 |
| industry: Emergency Management | -2.19e+04 | 9.92e+04 | -0.221 | 0.825 | -2.16e+05 | 1.72e+05 |
| industry: Energy | 4.66e+04 | 7.35e+04 | 0.634 | 0.526 | -9.75e+04 | 1.91e+05 |
| industry: Energy - Oil and Gas | 6.19e+04 | 9.92e+04 | 0.624 | 0.532 | -1.32e+05 | 2.56e+05 |
| industry: Energy (oil & gas & associated products, renewable power, etc) | 1.49e+05 | 9.92e+04 | 1.51 | 0.132 | -4.51e+04 | 3.44e+05 |
| industry: Energy (Oil & Gas) | 2.84e+04 | 9.92e+04 | 0.286 | 0.775 | -1.66e+05 | 2.23e+05 |
| industry: Energy Sector: Oil & Gas | -3.49e+04 | 9.92e+04 | -0.352 | 0.725 | -2.29e+05 | 1.6e+05 |
| industry: Energy Supplier | -2.49e+04 | 9.92e+04 | -0.251 | 0.802 | -2.19e+05 | 1.69e+05 |
| industry: Energy, Oil & Gas | 2.6e+04 | 9.92e+04 | 0.262 | 0.793 | -1.68e+05 | 2.2e+05 |
| industry: Energy: oil & gas | -7.01e+04 | 9.92e+04 | -0.706 | 0.48 | -2.65e+05 | 1.24e+05 |
| industry: Energy/oil | 4.32e+03 | 9.92e+04 | 0.044 | 0.965 | -1.9e+05 | 1.99e+05 |
| industry: Engineering and Environmental Consulting | -2.29e+04 | 9.92e+04 | -0.231 | 0.818 | -2.17e+05 | 1.71e+05 |
| industry: Engineering or Manufacturing | 1.15e+04 | 7.01e+04 | 0.164 | 0.87 | -1.26e+05 | 1.49e+05 |
| industry: Entertainment | 3.9e+04 | 7.03e+04 | 0.555 | 0.579 | -9.88e+04 | 1.77e+05 |
| industry: Entertainment data | 448 | 9.93e+04 | 0.005 | 0.996 | -1.94e+05 | 1.95e+05 |
| industry: Env. Consulting | -2.33e+04 | 9.92e+04 | -0.235 | 0.814 | -2.18e+05 | 1.71e+05 |
| industry: Environment | -2.18e+04 | 9.92e+04 | -0.22 | 0.826 | -2.16e+05 | 1.73e+05 |
| industry: Environment, health, and safety | -4.86e+03 | 9.92e+04 | -0.049 | 0.961 | -1.99e+05 | 1.9e+05 |
| industry: environmental | -9.99e+03 | 9.92e+04 | -0.101 | 0.92 | -2.04e+05 | 1.84e+05 |
| industry: Environmental | -1.37e+04 | 7.32e+04 | -0.187 | 0.851 | -1.57e+05 | 1.3e+05 |
| industry: environmental compliance | -9.81e+03 | 9.92e+04 | -0.099 | 0.921 | -2.04e+05 | 1.85e+05 |
| industry: Environmental compliance | -1.2e+04 | 8.59e+04 | -0.139 | 0.889 | -1.8e+05 | 1.56e+05 |
| industry: environmental compliance/engineering | -632 | 9.92e+04 | -0.006 | 0.995 | -1.95e+05 | 1.94e+05 |
| industry: Environmental Consultanting | -1.56e+04 | 9.92e+04 | -0.158 | 0.875 | -2.1e+05 | 1.79e+05 |
| industry: Environmental consulting | -3.29e+04 | 8.59e+04 | -0.383 | 0.702 | -2.01e+05 | 1.35e+05 |
| industry: Environmental Consulting | -9.71e+03 | 7.26e+04 | -0.134 | 0.894 | -1.52e+05 | 1.33e+05 |
| industry: Environmental Health + Pest Control | -1.7e+04 | 9.92e+04 | -0.171 | 0.864 | -2.11e+05 | 1.77e+05 |
| industry: environmental health and safety | -1.71e+04 | 9.92e+04 | -0.172 | 0.863 | -2.11e+05 | 1.77e+05 |
| industry: Environmental health and safety | -3.1e+04 | 9.93e+04 | -0.312 | 0.755 | -2.26e+05 | 1.64e+05 |
| industry: Environmental health and safety compliance | -1.97e+04 | 9.92e+04 | -0.199 | 0.842 | -2.14e+05 | 1.75e+05 |
| industry: Environmental Planning | 2.64e+03 | 9.92e+04 | 0.027 | 0.979 | -1.92e+05 | 1.97e+05 |
| industry: Environmental regulation | -5.25e+04 | 9.92e+04 | -0.529 | 0.597 | -2.47e+05 | 1.42e+05 |
| industry: Environmental Restoration | -3.46e+04 | 9.92e+04 | -0.349 | 0.727 | -2.29e+05 | 1.6e+05 |
| industry: Environmental Science | -1.68e+03 | 8.59e+04 | -0.02 | 0.984 | -1.7e+05 | 1.67e+05 |
| industry: Environmental sciences | -6.09e+03 | 9.92e+04 | -0.061 | 0.951 | -2e+05 | 1.88e+05 |
| industry: Environmental Sciences | -6.6e+03 | 9.91e+04 | -0.067 | 0.947 | -2.01e+05 | 1.88e+05 |
| industry: Environmental Services | -2.32e+04 | 9.92e+04 | -0.234 | 0.815 | -2.18e+05 | 1.71e+05 |
| industry: Environmental survey | -6.89e+04 | 9.92e+04 | -0.695 | 0.487 | -2.63e+05 | 1.25e+05 |
| industry: Environmental/Cultural Resource Management | -3e+04 | 9.92e+04 | -0.303 | 0.762 | -2.24e+05 | 1.64e+05 |
| industry: Environmnetal | -1.55e+04 | 9.92e+04 | -0.156 | 0.876 | -2.1e+05 | 1.79e+05 |
| industry: Executive Leadership Servis | -2.76e+03 | 9.92e+04 | -0.028 | 0.978 | -1.97e+05 | 1.92e+05 |
| industry: Executive search | -5.24e+03 | 9.92e+04 | -0.053 | 0.958 | -2e+05 | 1.89e+05 |
| industry: Executive Search | 2.42e+03 | 9.92e+04 | 0.024 | 0.981 | -1.92e+05 | 1.97e+05 |
| industry: Facilities | -1.35e+04 | 9.92e+04 | -0.136 | 0.892 | -2.08e+05 | 1.81e+05 |
| industry: Family Office | 3.42e+04 | 9.92e+04 | 0.345 | 0.73 | -1.6e+05 | 2.29e+05 |
| industry: Fashion | 1.62e+04 | 8.59e+04 | 0.189 | 0.85 | -1.52e+05 | 1.85e+05 |
| industry: Fashion/e-commerce | -1.61e+04 | 9.91e+04 | -0.162 | 0.871 | -2.1e+05 | 1.78e+05 |
| industry: Fast casual restaurant | -3.84e+04 | 9.92e+04 | -0.387 | 0.699 | -2.33e+05 | 1.56e+05 |
| industry: Fast Food | -3.61e+04 | 9.93e+04 | -0.364 | 0.716 | -2.31e+05 | 1.58e+05 |
| industry: Federal Contracting/Business Development | -2.29e+04 | 9.92e+04 | -0.231 | 0.817 | -2.17e+05 | 1.71e+05 |
| industry: Federal Government Contracting | 1.82e+04 | 9.92e+04 | 0.184 | 0.854 | -1.76e+05 | 2.13e+05 |
| industry: Film Post-Production | 1.17e+04 | 9.92e+04 | 0.118 | 0.906 | -1.83e+05 | 2.06e+05 |
| industry: finance | 4.19e+04 | 9.92e+04 | 0.423 | 0.673 | -1.52e+05 | 2.36e+05 |
| industry: Finance | -8.82e+03 | 8.59e+04 | -0.103 | 0.918 | -1.77e+05 | 1.6e+05 |
| industry: Finance/Investment Management but in legal/compliance, so back-office | 5.74e+04 | 9.92e+04 | 0.578 | 0.563 | -1.37e+05 | 2.52e+05 |
| industry: Fintech | 2.21e+04 | 8.1e+04 | 0.273 | 0.785 | -1.37e+05 | 1.81e+05 |
| industry: FinTech/Payment Processing | -2.61e+04 | 9.91e+04 | -0.264 | 0.792 | -2.2e+05 | 1.68e+05 |
| industry: Fire protection | -4.86e+03 | 9.92e+04 | -0.049 | 0.961 | -1.99e+05 | 1.9e+05 |
| industry: fitness | -3.88e+04 | 9.92e+04 | -0.391 | 0.696 | -2.33e+05 | 1.56e+05 |
| industry: Fitness | -3.72e+04 | 7.68e+04 | -0.484 | 0.628 | -1.88e+05 | 1.13e+05 |
| industry: Fitness & Entertainment | -4.48e+04 | 1e+05 | -0.447 | 0.655 | -2.41e+05 | 1.51e+05 |
| industry: Food | 4.82e+04 | 9.94e+04 | 0.485 | 0.627 | -1.47e+05 | 2.43e+05 |
| industry: Food & Beverage | 1.04e+05 | 8.1e+04 | 1.29 | 0.198 | -5.44e+04 | 2.63e+05 |
| industry: Food & Beverage production | -1.62e+03 | 9.92e+04 | -0.016 | 0.987 | -1.96e+05 | 1.93e+05 |
| industry: Food & Beverages | -3.31e+04 | 9.92e+04 | -0.334 | 0.739 | -2.27e+05 | 1.61e+05 |
| industry: Food & Nutrition | 3.1e+04 | 9.93e+04 | 0.312 | 0.755 | -1.64e+05 | 2.26e+05 |
| industry: Food and Beverage | -4.42e+03 | 9.92e+04 | -0.045 | 0.964 | -1.99e+05 | 1.9e+05 |
| industry: Food and Flavor | 13.8 | 9.92e+04 | 0 | 1 | -1.94e+05 | 1.94e+05 |
| industry: Food demos | -7.57e+04 | 9.95e+04 | -0.761 | 0.447 | -2.71e+05 | 1.19e+05 |
| industry: Food Distribution | -3.32e+04 | 9.91e+04 | -0.335 | 0.737 | -2.28e+05 | 1.61e+05 |
| industry: Food industry | -5.27e+03 | 9.91e+04 | -0.053 | 0.958 | -2e+05 | 1.89e+05 |
| industry: Food Industry | -2.07e+04 | 8.59e+04 | -0.241 | 0.809 | -1.89e+05 | 1.48e+05 |
| industry: Food manufacturing | -2.3e+04 | 8.1e+04 | -0.284 | 0.776 | -1.82e+05 | 1.36e+05 |
| industry: Food Manufacturing | -7.12e+03 | 8.59e+04 | -0.083 | 0.934 | -1.75e+05 | 1.61e+05 |
| industry: Food processing | -2.26e+04 | 9.91e+04 | -0.228 | 0.819 | -2.17e+05 | 1.72e+05 |
| industry: Food production | -3.01e+04 | 9.92e+04 | -0.303 | 0.762 | -2.24e+05 | 1.64e+05 |
| industry: Food service | -2.78e+04 | 7.68e+04 | -0.361 | 0.718 | -1.78e+05 | 1.23e+05 |
| industry: Food Service | -2.66e+04 | 8.1e+04 | -0.328 | 0.743 | -1.85e+05 | 1.32e+05 |
| industry: Food Service --- Baking | -3.24e+04 | 9.92e+04 | -0.326 | 0.744 | -2.27e+05 | 1.62e+05 |
| industry: Food/Beverage Manufacturing- Quality/Laboratory | -3.21e+04 | 9.91e+04 | -0.324 | 0.746 | -2.26e+05 | 1.62e+05 |
| industry: Food/Quick Service Restaurant (QSR) | -9.27e+03 | 9.92e+04 | -0.093 | 0.926 | -2.04e+05 | 1.85e+05 |
| industry: Foodservice | 3.04e+04 | 9.91e+04 | 0.306 | 0.759 | -1.64e+05 | 2.25e+05 |
| industry: For profit education | -1.86e+04 | 9.91e+04 | -0.188 | 0.851 | -2.13e+05 | 1.76e+05 |
| industry: Forensics | -1.96e+04 | 9.91e+04 | -0.198 | 0.843 | -2.14e+05 | 1.75e+05 |
| industry: Freelance Journalism | -6.19e+04 | 9.92e+04 | -0.624 | 0.533 | -2.56e+05 | 1.33e+05 |
| industry: Freelance/Self-Employed Consultant | 1.99e+04 | 9.92e+04 | 0.2 | 0.841 | -1.75e+05 | 2.14e+05 |
| industry: Funding Intermediary | 4.85e+04 | 9.93e+04 | 0.488 | 0.625 | -1.46e+05 | 2.43e+05 |
| industry: Fundraising | 1.49e+04 | 7.84e+04 | 0.19 | 0.849 | -1.39e+05 | 1.69e+05 |
| industry: Fundraising for a university | 2.57e+03 | 9.92e+04 | 0.026 | 0.979 | -1.92e+05 | 1.97e+05 |
| industry: Fundraising in Higher Education; nonclinical, nonacademic | -2.88e+04 | 9.93e+04 | -0.29 | 0.772 | -2.23e+05 | 1.66e+05 |
| industry: Funeral | -2.6e+04 | 9.92e+04 | -0.263 | 0.793 | -2.21e+05 | 1.68e+05 |
| industry: Funeral Service | -3.65e+04 | 8.59e+04 | -0.425 | 0.671 | -2.05e+05 | 1.32e+05 |
| industry: funeral services | -6.15e+04 | 9.92e+04 | -0.619 | 0.536 | -2.56e+05 | 1.33e+05 |
| industry: Gambling | -1.81e+04 | 9.92e+04 | -0.182 | 0.855 | -2.12e+05 | 1.76e+05 |
| industry: Game development | 1.19e+04 | 9.92e+04 | 0.12 | 0.905 | -1.83e+05 | 2.06e+05 |
| industry: Game Development | 1.77e+04 | 9.91e+04 | 0.179 | 0.858 | -1.77e+05 | 2.12e+05 |
| industry: Gaming | 4.75e+04 | 8.59e+04 | 0.553 | 0.58 | -1.21e+05 | 2.16e+05 |
| industry: Gaming (Gambling) | -3.41e+04 | 9.92e+04 | -0.344 | 0.731 | -2.29e+05 | 1.6e+05 |
| industry: Gas & Oil | -1.58e+04 | 9.92e+04 | -0.159 | 0.874 | -2.1e+05 | 1.79e+05 |
| industry: Geospatial | -2.79e+04 | 9.92e+04 | -0.281 | 0.778 | -2.22e+05 | 1.66e+05 |
| industry: Global Health Consulting | 1.94e+04 | 9.92e+04 | 0.196 | 0.845 | -1.75e+05 | 2.14e+05 |
| industry: Global Mobility | -1.74e+04 | 9.92e+04 | -0.175 | 0.861 | -2.12e+05 | 1.77e+05 |
| industry: Government | -1.81e+04 | 9.92e+04 | -0.182 | 0.855 | -2.12e+05 | 1.76e+05 |
| industry: Government Affairs/Lobbying | 6.33e+04 | 8.59e+04 | 0.737 | 0.461 | -1.05e+05 | 2.32e+05 |
| industry: Government and Public Administration | -9.78e+03 | 7.01e+04 | -0.139 | 0.889 | -1.47e+05 | 1.28e+05 |
| industry: Government contracting | 8.52e+03 | 8.59e+04 | 0.099 | 0.921 | -1.6e+05 | 1.77e+05 |
| industry: Government Contracting | 3.27e+04 | 8.1e+04 | 0.404 | 0.687 | -1.26e+05 | 1.91e+05 |
| industry: Government contracting (data analytics and program evaluations) | 7.5e+03 | 9.92e+04 | 0.076 | 0.94 | -1.87e+05 | 2.02e+05 |
| industry: Government Contracting R&D | 1.54e+04 | 9.91e+04 | 0.155 | 0.877 | -1.79e+05 | 2.1e+05 |
| industry: Government contractor | -2.57e+03 | 7.85e+04 | -0.033 | 0.974 | -1.56e+05 | 1.51e+05 |
| industry: Government Contractor | -1.96e+04 | 8.6e+04 | -0.228 | 0.82 | -1.88e+05 | 1.49e+05 |
| industry: Government Contractor (R&D) | -1.03e+04 | 9.91e+04 | -0.104 | 0.918 | -2.05e+05 | 1.84e+05 |
| industry: government contractor, international development | 3.71e+04 | 9.92e+04 | 0.374 | 0.708 | -1.57e+05 | 2.31e+05 |
| industry: Government relations | -3.6e+04 | 9.92e+04 | -0.363 | 0.717 | -2.3e+05 | 1.58e+05 |
| industry: Government Relations | 2.99e+04 | 8.1e+04 | 0.369 | 0.712 | -1.29e+05 | 1.89e+05 |
| industry: Government Relations/Lobbying | 9.93e+03 | 9.92e+04 | 0.1 | 0.92 | -1.84e+05 | 2.04e+05 |
| industry: Government- Scientist | 2.13e+03 | 9.92e+04 | 0.021 | 0.983 | -1.92e+05 | 1.96e+05 |
| industry: Govt contractor - not direct govt but they pay my company who in turn pays me | 1.54e+04 | 9.91e+04 | 0.155 | 0.877 | -1.79e+05 | 2.1e+05 |
| industry: Govt contractor - not directly govt but they pay me | 1.54e+04 | 9.91e+04 | 0.155 | 0.877 | -1.79e+05 | 2.1e+05 |
| industry: Govtech Software as a Service | 4.33e+03 | 9.92e+04 | 0.044 | 0.965 | -1.9e+05 | 1.99e+05 |
| industry: Graduate assistant and also events | -5.73e+04 | 9.92e+04 | -0.578 | 0.563 | -2.52e+05 | 1.37e+05 |
| industry: Grantwriting Consultants | -3.87e+03 | 9.92e+04 | -0.039 | 0.969 | -1.98e+05 | 1.9e+05 |
| industry: Grocery delivery | -2.24e+04 | 9.92e+04 | -0.226 | 0.821 | -2.17e+05 | 1.72e+05 |
| industry: Grocery Distribution | -2.26e+04 | 8.59e+04 | -0.263 | 0.793 | -1.91e+05 | 1.46e+05 |
| industry: Gyms | -3.79e+04 | 9.92e+04 | -0.382 | 0.703 | -2.32e+05 | 1.56e+05 |
| industry: Haz/Ind/Rad Waste Management | 6.87e+03 | 9.92e+04 | 0.069 | 0.945 | -1.88e+05 | 2.01e+05 |
| industry: Health and fitness | -4.1e+04 | 9.91e+04 | -0.413 | 0.68 | -2.35e+05 | 1.53e+05 |
| industry: Health care | 2.36e+03 | 7.01e+04 | 0.034 | 0.973 | -1.35e+05 | 1.4e+05 |
| industry: Health Insurance | -1.88e+04 | 9.92e+04 | -0.189 | 0.85 | -2.13e+05 | 1.76e+05 |
| industry: Health Research | -1.75e+04 | 9.92e+04 | -0.176 | 0.86 | -2.12e+05 | 1.77e+05 |
| industry: Healthcare Information Technology | -1.91e+04 | 9.92e+04 | -0.192 | 0.847 | -2.13e+05 | 1.75e+05 |
| industry: Healthcare IT | 3.32e+04 | 8.1e+04 | 0.411 | 0.681 | -1.25e+05 | 1.92e+05 |
| industry: Healthcare technology | -4.19e+03 | 9.92e+04 | -0.042 | 0.966 | -1.99e+05 | 1.9e+05 |
| industry: Heritage | -2.01e+04 | 9.92e+04 | -0.202 | 0.84 | -2.14e+05 | 1.74e+05 |
| industry: High end outdoor furniture manufacturer | -3.16e+04 | 9.91e+04 | -0.319 | 0.75 | -2.26e+05 | 1.63e+05 |
| industry: Higher education fundraising | -3.89e+04 | 9.92e+04 | -0.392 | 0.695 | -2.33e+05 | 1.55e+05 |
| industry: Higher education/Libraries | -2.58e+04 | 9.92e+04 | -0.26 | 0.794 | -2.2e+05 | 1.69e+05 |
| industry: Historic Preservation | -2.29e+04 | 9.92e+04 | -0.231 | 0.818 | -2.17e+05 | 1.71e+05 |
| industry: Horticulture | -4.46e+04 | 9.92e+04 | -0.45 | 0.653 | -2.39e+05 | 1.5e+05 |
| industry: Hospital | -995 | 9.92e+04 | -0.01 | 0.992 | -1.95e+05 | 1.93e+05 |
| industry: Hospitality & Events | -1.15e+04 | 7.03e+04 | -0.164 | 0.87 | -1.49e+05 | 1.26e+05 |
| industry: Household Services | -3.72e+04 | 9.92e+04 | -0.375 | 0.708 | -2.32e+05 | 1.57e+05 |
| industry: Housekeeper/cook | -3.81e+04 | 9.92e+04 | -0.384 | 0.701 | -2.32e+05 | 1.56e+05 |
| industry: HRO | -3.23e+04 | 9.92e+04 | -0.326 | 0.745 | -2.27e+05 | 1.62e+05 |
| industry: Human Capital Management | 5.89e+04 | 9.92e+04 | 0.594 | 0.552 | -1.35e+05 | 2.53e+05 |
| industry: Human Resources | -2.7e+04 | 8.59e+04 | -0.315 | 0.753 | -1.95e+05 | 1.41e+05 |
| industry: Human services | -5.54e+04 | 9.92e+04 | -0.558 | 0.577 | -2.5e+05 | 1.39e+05 |
| industry: Hybrid Nonprofit Higher Education (we are part of a university but our entire budget comes from grants) | -6.14e+04 | 9.92e+04 | -0.618 | 0.536 | -2.56e+05 | 1.33e+05 |
| industry: I have two jobs. Marketing / Business | 6.62e+04 | 9.92e+04 | 0.667 | 0.505 | -1.28e+05 | 2.61e+05 |
| industry: I work for Indeed.com | -2.28e+04 | 9.92e+04 | -0.229 | 0.818 | -2.17e+05 | 1.72e+05 |
| industry: I work in the finance function of a large global conglomerate | 1.15e+04 | 9.92e+04 | 0.116 | 0.908 | -1.83e+05 | 2.06e+05 |
| industry: Industrial Cleaning & Non Hazardous Transport | -1.25e+04 | 9.92e+04 | -0.126 | 0.9 | -2.07e+05 | 1.82e+05 |
| industry: Industrial Hygiene | 1.91e+03 | 9.92e+04 | 0.019 | 0.985 | -1.92e+05 | 1.96e+05 |
| industry: Industrial Supply | -2.72e+04 | 9.92e+04 | -0.274 | 0.784 | -2.22e+05 | 1.67e+05 |
| industry: Information | 4.42e+04 | 9.92e+04 | 0.446 | 0.655 | -1.5e+05 | 2.39e+05 |
| industry: Information sciences | -2.07e+04 | 9.92e+04 | -0.208 | 0.835 | -2.15e+05 | 1.74e+05 |
| industry: Information services (libraries) | -3.06e+04 | 9.92e+04 | -0.309 | 0.757 | -2.25e+05 | 1.64e+05 |
| industry: Information services (library) | -4.95e+04 | 9.91e+04 | -0.499 | 0.618 | -2.44e+05 | 1.45e+05 |
| industry: Information Services/Libraries | -4.49e+04 | 9.92e+04 | -0.453 | 0.651 | -2.39e+05 | 1.49e+05 |
| industry: Information Technology | -1.93e+04 | 8.6e+04 | -0.225 | 0.822 | -1.88e+05 | 1.49e+05 |
| industry: Information technology (IT) | 2.01e+04 | 9.92e+04 | 0.203 | 0.839 | -1.74e+05 | 2.15e+05 |
| industry: Instructional Design and Training | 1.32e+04 | 9.92e+04 | 0.134 | 0.894 | -1.81e+05 | 2.08e+05 |
| industry: Instructional Design, Aviation Industry | -3.58e+04 | 9.92e+04 | -0.361 | 0.718 | -2.3e+05 | 1.59e+05 |
| industry: Instructional Designer | -4.64e+03 | 9.91e+04 | -0.047 | 0.963 | -1.99e+05 | 1.9e+05 |
| industry: Insurance | 699 | 7.02e+04 | 0.01 | 0.992 | -1.37e+05 | 1.38e+05 |
| industry: Intelligence | 2.22e+04 | 9.92e+04 | 0.223 | 0.823 | -1.72e+05 | 2.17e+05 |
| industry: Interior Design (commercial) | -1.08e+04 | 9.92e+04 | -0.109 | 0.913 | -2.05e+05 | 1.84e+05 |
| industry: Interior landscaping | -2.64e+04 | 9.92e+04 | -0.266 | 0.79 | -2.21e+05 | 1.68e+05 |
| industry: International development | -1.97e+04 | 8.59e+04 | -0.229 | 0.818 | -1.88e+05 | 1.49e+05 |
| industry: International Development | -6.71e+03 | 8.1e+04 | -0.083 | 0.934 | -1.65e+05 | 1.52e+05 |
| industry: International development (multilateral donor) | 2.33e+04 | 9.92e+04 | 0.235 | 0.814 | -1.71e+05 | 2.18e+05 |
| industry: Internet | -7.87e+03 | 9.92e+04 | -0.079 | 0.937 | -2.02e+05 | 1.86e+05 |
| industry: Investing | -2.24e+04 | 9.92e+04 | -0.226 | 0.821 | -2.17e+05 | 1.72e+05 |
| industry: IT | 1.69e+04 | 7.84e+04 | 0.216 | 0.829 | -1.37e+05 | 1.71e+05 |
| industry: IT MSP | -1.36e+04 | 9.92e+04 | -0.137 | 0.891 | -2.08e+05 | 1.81e+05 |
| industry: IT Security | -3.09e+04 | 9.91e+04 | -0.312 | 0.755 | -2.25e+05 | 1.63e+05 |
| industry: Janitorial | -1.32e+04 | 9.93e+04 | -0.133 | 0.894 | -2.08e+05 | 1.81e+05 |
| industry: Journalism | -4.34e+04 | 8.1e+04 | -0.536 | 0.592 | -2.02e+05 | 1.15e+05 |
| industry: Lab Science (Biotech) | -3.27e+04 | 9.92e+04 | -0.33 | 0.742 | -2.27e+05 | 1.62e+05 |
| industry: Labor | 1.35e+03 | 9.91e+04 | 0.014 | 0.989 | -1.93e+05 | 1.96e+05 |
| industry: labor union | -1.06e+04 | 9.92e+04 | -0.107 | 0.915 | -2.05e+05 | 1.84e+05 |
| industry: Labor Union | 3.69e+04 | 7.84e+04 | 0.471 | 0.638 | -1.17e+05 | 1.91e+05 |
| industry: Laboratory research | -1.73e+04 | 9.91e+04 | -0.174 | 0.862 | -2.12e+05 | 1.77e+05 |
| industry: Landscape Architecture | -6.09e+03 | 9.92e+04 | -0.061 | 0.951 | -2e+05 | 1.88e+05 |
| industry: Landscape Contracting | -2.7e+04 | 9.92e+04 | -0.273 | 0.785 | -2.21e+05 | 1.67e+05 |
| industry: Landscaping | -7.85e+03 | 9.92e+04 | -0.079 | 0.937 | -2.02e+05 | 1.87e+05 |
| industry: Landscaping/Tree Work | 2.63e+03 | 9.95e+04 | 0.026 | 0.979 | -1.92e+05 | 1.98e+05 |
| industry: Language Services | 2.17e+04 | 9.91e+04 | 0.219 | 0.826 | -1.73e+05 | 2.16e+05 |
| industry: Language Services company, unsure the broad category to use. Our clients are branding agencies, and their clients are frequently (but not always) pharmaceutical companies. | -7.68e+03 | 9.93e+04 | -0.077 | 0.938 | -2.02e+05 | 1.87e+05 |
| industry: Laundry and Rental | -2.29e+04 | 9.92e+04 | -0.231 | 0.817 | -2.17e+05 | 1.71e+05 |
| industry: Law | 5.67e+03 | 7.02e+04 | 0.081 | 0.936 | -1.32e+05 | 1.43e+05 |
| industry: Law Enforcement & Security | -1.49e+04 | 7.14e+04 | -0.209 | 0.835 | -1.55e+05 | 1.25e+05 |
| industry: Learning & Development | -4.31e+04 | 9.92e+04 | -0.435 | 0.664 | -2.38e+05 | 1.51e+05 |
| industry: Learning and Development | -3.09e+04 | 9.92e+04 | -0.312 | 0.755 | -2.25e+05 | 1.63e+05 |
| industry: Legal Services | 3.18e+03 | 9.92e+04 | 0.032 | 0.974 | -1.91e+05 | 1.98e+05 |
| industry: Leisure, Sport & Tourism | -1.38e+04 | 7.06e+04 | -0.195 | 0.845 | -1.52e+05 | 1.25e+05 |
| industry: Librarian | -3.97e+04 | 7.68e+04 | -0.517 | 0.605 | -1.9e+05 | 1.11e+05 |
| industry: Librarian and Assistant Manager of a library | -2.95e+04 | 9.92e+04 | -0.297 | 0.767 | -2.24e+05 | 1.65e+05 |
| industry: Librarian in legal setting | -1.01e+04 | 9.92e+04 | -0.102 | 0.919 | -2.04e+05 | 1.84e+05 |
| industry: librarian--Contractor for NASA | -5.3e+04 | 9.92e+04 | -0.534 | 0.593 | -2.47e+05 | 1.41e+05 |
| industry: libraries | -3.2e+04 | 9.92e+04 | -0.323 | 0.747 | -2.26e+05 | 1.62e+05 |
| industry: Libraries | -3.1e+04 | 7.09e+04 | -0.437 | 0.662 | -1.7e+05 | 1.08e+05 |
| industry: LIBRARIES | -2.61e+04 | 9.92e+04 | -0.263 | 0.793 | -2.2e+05 | 1.68e+05 |
| industry: Libraries (Medical) | -3.32e+03 | 9.92e+04 | -0.033 | 0.973 | -1.98e+05 | 1.91e+05 |
| industry: Libraries (public) | -4.61e+04 | 9.92e+04 | -0.465 | 0.642 | -2.4e+05 | 1.48e+05 |
| industry: Libraries (Public) | -5.36e+04 | 9.92e+04 | -0.54 | 0.589 | -2.48e+05 | 1.41e+05 |
| industry: Libraries / Archives / Information | -2.75e+04 | 9.92e+04 | -0.277 | 0.782 | -2.22e+05 | 1.67e+05 |
| industry: Libraries & Archives | -3.2e+04 | 8.59e+04 | -0.372 | 0.71 | -2e+05 | 1.36e+05 |
| industry: Libraries and Archives | -2.88e+04 | 8.59e+04 | -0.335 | 0.737 | -1.97e+05 | 1.4e+05 |
| industry: Libraries and Archives (Academic) | -4.51e+04 | 9.92e+04 | -0.455 | 0.649 | -2.4e+05 | 1.49e+05 |
| industry: Libraries/Archives | -2.58e+04 | 9.93e+04 | -0.26 | 0.795 | -2.2e+05 | 1.69e+05 |
| industry: Libraries/Museums/Archives | -4.73e+04 | 9.92e+04 | -0.477 | 0.634 | -2.42e+05 | 1.47e+05 |
| industry: library | -4.48e+04 | 8.1e+04 | -0.554 | 0.58 | -2.04e+05 | 1.14e+05 |
| industry: Library | -3.42e+04 | 7.08e+04 | -0.484 | 0.629 | -1.73e+05 | 1.05e+05 |
| industry: Library (its a non-profit and its a govt job - how would I list that? Not all libraries are govt jobs) | -5.04e+04 | 9.92e+04 | -0.507 | 0.612 | -2.45e+05 | 1.44e+05 |
| industry: Library and Information Science | -5.17e+04 | 9.92e+04 | -0.521 | 0.602 | -2.46e+05 | 1.43e+05 |
| industry: Library and Information Services | -4.29e+04 | 9.92e+04 | -0.433 | 0.665 | -2.37e+05 | 1.51e+05 |
| industry: Library at a University | -4.91e+04 | 9.92e+04 | -0.495 | 0.62 | -2.44e+05 | 1.45e+05 |
| industry: Library Page (Public county library) | -1.74e+04 | 9.92e+04 | -0.175 | 0.861 | -2.12e+05 | 1.77e+05 |
| industry: Library science / part-time work/study | -1.69e+04 | 9.93e+04 | -0.17 | 0.865 | -2.12e+05 | 1.78e+05 |
| industry: Library--public | -1.6e+04 | 9.92e+04 | -0.161 | 0.872 | -2.1e+05 | 1.78e+05 |
| industry: Library/Archive | -4.33e+04 | 9.92e+04 | -0.437 | 0.662 | -2.38e+05 | 1.51e+05 |
| industry: Library/Archive/Research Center | -1.9e+04 | 9.92e+04 | -0.191 | 0.848 | -2.13e+05 | 1.75e+05 |
| industry: Library/archives | -4.14e+04 | 8.59e+04 | -0.482 | 0.63 | -2.1e+05 | 1.27e+05 |
| industry: Life science capability development | -3.81e+04 | 9.92e+04 | -0.384 | 0.701 | -2.32e+05 | 1.56e+05 |
| industry: life sciences | -1.06e+04 | 9.91e+04 | -0.107 | 0.915 | -2.05e+05 | 1.84e+05 |
| industry: Life Sciences | 6.94e+03 | 7.84e+04 | 0.089 | 0.929 | -1.47e+05 | 1.61e+05 |
| industry: Life sciences (not in academia) | -9.91e+03 | 9.92e+04 | -0.1 | 0.92 | -2.04e+05 | 1.84e+05 |
| industry: Lobbying and consulting | 9.73e+03 | 9.91e+04 | 0.098 | 0.922 | -1.85e+05 | 2.04e+05 |
| industry: Logistics | -1.19e+04 | 9.91e+04 | -0.12 | 0.905 | -2.06e+05 | 1.82e+05 |
| industry: Low-Voltage Equipment | -5.77e+04 | 9.92e+04 | -0.582 | 0.561 | -2.52e+05 | 1.37e+05 |
| industry: Luxury Fashion | -3.31e+04 | 9.92e+04 | -0.333 | 0.739 | -2.28e+05 | 1.61e+05 |
| industry: Management Consulting | 9.51e+04 | 9.92e+04 | 0.959 | 0.338 | -9.93e+04 | 2.89e+05 |
| industry: manufacturing | -1.11e+04 | 7.58e+04 | -0.146 | 0.884 | -1.6e+05 | 1.37e+05 |
| industry: Manufacturing | -1.54e+04 | 7.12e+04 | -0.217 | 0.828 | -1.55e+05 | 1.24e+05 |
| industry: MANUFACTURING | -3e+04 | 9.92e+04 | -0.303 | 0.762 | -2.25e+05 | 1.64e+05 |
| industry: Manufacturing : corporate admin support | -4.86e+03 | 9.92e+04 | -0.049 | 0.961 | -1.99e+05 | 1.9e+05 |
| industry: Manufacturing (medical devices) | -3.39e+04 | 9.92e+04 | -0.342 | 0.732 | -2.28e+05 | 1.6e+05 |
| industry: Manufacturing (personal care) | 3.01e+04 | 9.92e+04 | 0.304 | 0.761 | -1.64e+05 | 2.25e+05 |
| industry: Manufacturing (pharmaceuticals) | 1.27e+04 | 9.92e+04 | 0.128 | 0.898 | -1.82e+05 | 2.07e+05 |
| industry: Manufacturing and distributing | -4.58e+04 | 9.91e+04 | -0.462 | 0.644 | -2.4e+05 | 1.49e+05 |
| industry: Manufacturing, Chemical | -2.38e+03 | 9.95e+04 | -0.024 | 0.981 | -1.97e+05 | 1.93e+05 |
| industry: Manufacturing/Wholesale | -1.66e+04 | 9.92e+04 | -0.168 | 0.867 | -2.11e+05 | 1.78e+05 |
| industry: market research | -9.41e+03 | 8.59e+04 | -0.11 | 0.913 | -1.78e+05 | 1.59e+05 |
| industry: Market research | 4.06e+04 | 9.92e+04 | 0.409 | 0.682 | -1.54e+05 | 2.35e+05 |
| industry: Market Research | -6.67e+03 | 7.5e+04 | -0.089 | 0.929 | -1.54e+05 | 1.4e+05 |
| industry: Marketing at a Non Profit | 2.82e+03 | 1e+05 | 0.028 | 0.978 | -1.93e+05 | 1.99e+05 |
| industry: Marketing technology | -1.51e+04 | 9.92e+04 | -0.153 | 0.879 | -2.1e+05 | 1.79e+05 |
| industry: Marketing, Advertising & PR | 9.68e+03 | 7.02e+04 | 0.138 | 0.89 | -1.28e+05 | 1.47e+05 |
| industry: Media & Digital | 1.43e+03 | 7.02e+04 | 0.02 | 0.984 | -1.36e+05 | 1.39e+05 |
| industry: Medica education | -2.41e+03 | 9.92e+04 | -0.024 | 0.981 | -1.97e+05 | 1.92e+05 |
| industry: Medical communications | 3.73e+03 | 9.92e+04 | 0.038 | 0.97 | -1.91e+05 | 1.98e+05 |
| industry: Medical Communications | 2.54e+03 | 9.92e+04 | 0.026 | 0.98 | -1.92e+05 | 1.97e+05 |
| industry: Medical Device | 2.64e+04 | 8.59e+04 | 0.308 | 0.758 | -1.42e+05 | 1.95e+05 |
| industry: Medical Devices | -1.68e+04 | 8.1e+04 | -0.208 | 0.835 | -1.76e+05 | 1.42e+05 |
| industry: Medical Interpreter -(Spanish) | -7.4e+04 | 9.92e+04 | -0.746 | 0.456 | -2.68e+05 | 1.2e+05 |
| industry: Medical Library | -1.19e+04 | 9.92e+04 | -0.12 | 0.905 | -2.06e+05 | 1.82e+05 |
| industry: Medical Research | -1.41e+04 | 7.84e+04 | -0.179 | 0.858 | -1.68e+05 | 1.4e+05 |
| industry: Medical supply Wholesale & Warehousing | -4.19e+04 | 9.92e+04 | -0.422 | 0.673 | -2.36e+05 | 1.53e+05 |
| industry: Medical technology | -1.99e+04 | 9.92e+04 | -0.201 | 0.841 | -2.14e+05 | 1.74e+05 |
| industry: Medical Technology | 3.03e+04 | 8.59e+04 | 0.353 | 0.724 | -1.38e+05 | 1.99e+05 |
| industry: Medical/Pharmaceutical | 1.16e+05 | 9.92e+04 | 1.17 | 0.242 | -7.84e+04 | 3.1e+05 |
| industry: mental health | -4.68e+04 | 9.92e+04 | -0.472 | 0.637 | -2.41e+05 | 1.48e+05 |
| industry: Mental health | -7.64e+04 | 9.94e+04 | -0.768 | 0.442 | -2.71e+05 | 1.18e+05 |
| industry: Mental Health | -4.51e+04 | 9.92e+04 | -0.455 | 0.649 | -2.39e+05 | 1.49e+05 |
| industry: Mental health therapist | -2.43e+04 | 9.92e+04 | -0.245 | 0.807 | -2.19e+05 | 1.7e+05 |
| industry: Military | 2.05e+04 | 8.1e+04 | 0.253 | 0.8 | -1.38e+05 | 1.79e+05 |
| industry: Mining | -2.36e+03 | 8.1e+04 | -0.029 | 0.977 | -1.61e+05 | 1.56e+05 |
| industry: Mining/Mineral Exploration | -9.86e+03 | 9.92e+04 | -0.099 | 0.921 | -2.04e+05 | 1.85e+05 |
| industry: Mining/Resource Extraction | 7.11e+03 | 9.92e+04 | 0.072 | 0.943 | -1.87e+05 | 2.01e+05 |
| industry: Ministry | -4.36e+04 | 8.1e+04 | -0.538 | 0.591 | -2.02e+05 | 1.15e+05 |
| industry: mortgage | -7.27e+03 | 9.91e+04 | -0.073 | 0.942 | -2.02e+05 | 1.87e+05 |
| industry: Mortgage | -6.08e+03 | 8.59e+04 | -0.071 | 0.944 | -1.74e+05 | 1.62e+05 |
| industry: municipal (public) libraries | -3.19e+04 | 9.92e+04 | -0.321 | 0.748 | -2.26e+05 | 1.62e+05 |
| industry: Municipal Government (Library) | -2.35e+04 | 9.92e+04 | -0.237 | 0.812 | -2.18e+05 | 1.71e+05 |
| industry: Municipal library | -5.91e+04 | 9.93e+04 | -0.596 | 0.551 | -2.54e+05 | 1.35e+05 |
| industry: Museum | -3.65e+04 | 7.39e+04 | -0.494 | 0.621 | -1.81e+05 | 1.08e+05 |
| industry: Museum - Nonprofit | -3.29e+04 | 9.92e+04 | -0.331 | 0.74 | -2.27e+05 | 1.61e+05 |
| industry: Museum (<20 employees) | -3.61e+04 | 9.92e+04 | -0.364 | 0.716 | -2.3e+05 | 1.58e+05 |
| industry: Museum (University Affiliated) | -2.91e+04 | 9.92e+04 | -0.293 | 0.769 | -2.23e+05 | 1.65e+05 |
| industry: Museum education | -5.14e+04 | 9.92e+04 | -0.519 | 0.604 | -2.46e+05 | 1.43e+05 |
| industry: Museums | -4.38e+04 | 7.5e+04 | -0.584 | 0.559 | -1.91e+05 | 1.03e+05 |
| industry: museums & archives (not sure where this would fall) | -2.99e+04 | 9.91e+04 | -0.302 | 0.763 | -2.24e+05 | 1.64e+05 |
| industry: Museums: Nonprofit | -4.61e+04 | 9.92e+04 | -0.465 | 0.642 | -2.4e+05 | 1.48e+05 |
| industry: Music | -3.79e+04 | 9.92e+04 | -0.383 | 0.702 | -2.32e+05 | 1.56e+05 |
| industry: Music Licensing | -3.85e+04 | 9.92e+04 | -0.389 | 0.698 | -2.33e+05 | 1.56e+05 |
| industry: music therapy | 154 | 9.92e+04 | 0.002 | 0.999 | -1.94e+05 | 1.95e+05 |
| industry: Music, education | 2.13e+03 | 9.92e+04 | 0.021 | 0.983 | -1.92e+05 | 1.96e+05 |
| industry: Music: freelance, performing and education | -4.81e+04 | 9.92e+04 | -0.485 | 0.627 | -2.43e+05 | 1.46e+05 |
| industry: My company sells & services various types of printers, mostly Kyocera. | -2.27e+04 | 9.92e+04 | -0.229 | 0.819 | -2.17e+05 | 1.72e+05 |
| industry: National laboratory | 1.35e+04 | 9.92e+04 | 0.136 | 0.892 | -1.81e+05 | 2.08e+05 |
| industry: Natural resources | -2.15e+04 | 9.92e+04 | -0.217 | 0.828 | -2.16e+05 | 1.73e+05 |
| industry: Natural Resources | -1.53e+04 | 9.91e+04 | -0.154 | 0.878 | -2.1e+05 | 1.79e+05 |
| industry: Non profit theater | 1.01e+04 | 9.92e+04 | 0.102 | 0.919 | -1.84e+05 | 2.04e+05 |
| industry: Non Profit Theater | -3.57e+04 | 9.92e+04 | -0.36 | 0.719 | -2.3e+05 | 1.59e+05 |
| industry: Non-profit health care (I couldn’t select both) | -2.82e+04 | 9.92e+04 | -0.284 | 0.776 | -2.23e+05 | 1.66e+05 |
| industry: Non-profit Theatre | -1.26e+04 | 9.91e+04 | -0.127 | 0.899 | -2.07e+05 | 1.82e+05 |
| industry: Nonprofit - legal department | 3.73e+03 | 9.91e+04 | 0.038 | 0.97 | -1.91e+05 | 1.98e+05 |
| industry: Nonprofit - LORT D Theater | -1.28e+04 | 9.92e+04 | -0.13 | 0.897 | -2.07e+05 | 1.82e+05 |
| industry: Nonprofit association | -1.56e+04 | 9.91e+04 | -0.158 | 0.875 | -2.1e+05 | 1.79e+05 |
| industry: Nonprofit scholarly society publisher | 4.46e+03 | 9.92e+04 | 0.045 | 0.964 | -1.9e+05 | 1.99e+05 |
| industry: Nonprofits | -1.42e+04 | 7.01e+04 | -0.202 | 0.84 | -1.52e+05 | 1.23e+05 |
| industry: Nuclear research | 2.91e+04 | 9.92e+04 | 0.293 | 0.769 | -1.65e+05 | 2.23e+05 |
| industry: oceanography research | -1.26e+04 | 9.92e+04 | -0.127 | 0.899 | -2.07e+05 | 1.82e+05 |
| industry: Office Admin | -1.14e+04 | 9.92e+04 | -0.115 | 0.908 | -2.06e+05 | 1.83e+05 |
| industry: Office Administration | -4.39e+04 | 9.92e+04 | -0.443 | 0.658 | -2.38e+05 | 1.5e+05 |
| industry: oil & gas | -2.9e+03 | 9.92e+04 | -0.029 | 0.977 | -1.97e+05 | 1.92e+05 |
| industry: Oil & gas | -2.79e+04 | 8.59e+04 | -0.324 | 0.746 | -1.96e+05 | 1.4e+05 |
| industry: Oil & Gas | 1.23e+04 | 7.68e+04 | 0.16 | 0.873 | -1.38e+05 | 1.63e+05 |
| industry: Oil & Gas - Non Destructive Testing | -1.82e+04 | 9.92e+04 | -0.184 | 0.854 | -2.13e+05 | 1.76e+05 |
| industry: Oil and gas | 7.97e+03 | 8.61e+04 | 0.093 | 0.926 | -1.61e+05 | 1.77e+05 |
| industry: Oil and Gas | 4.45e+04 | 7.5e+04 | 0.593 | 0.553 | -1.02e+05 | 1.91e+05 |
| industry: Oil and Gas Safety Training | -4.19e+04 | 9.92e+04 | -0.422 | 0.673 | -2.36e+05 | 1.52e+05 |
| industry: Online education | 1.92e+04 | 9.92e+04 | 0.193 | 0.847 | -1.75e+05 | 2.14e+05 |
| industry: Online learning | -4.78e+04 | 9.92e+04 | -0.482 | 0.63 | -2.42e+05 | 1.47e+05 |
| industry: Operational Training | -4.81e+03 | 9.92e+04 | -0.048 | 0.961 | -1.99e+05 | 1.9e+05 |
| industry: Operations | 2.51e+04 | 9.92e+04 | 0.253 | 0.8 | -1.69e+05 | 2.19e+05 |
| industry: Organized Labor | -1.06e+04 | 8.59e+04 | -0.124 | 0.901 | -1.79e+05 | 1.58e+05 |
| industry: Outdoor industry/repair and maintenance | -3.05e+04 | 9.92e+04 | -0.307 | 0.759 | -2.25e+05 | 1.64e+05 |
| industry: Outsourcing Services | -1.62e+03 | 9.92e+04 | -0.016 | 0.987 | -1.96e+05 | 1.93e+05 |
| industry: parking | 6.8e+03 | 9.93e+04 | 0.068 | 0.945 | -1.88e+05 | 2.01e+05 |
| industry: Parks and recreation, land management but with customer service included | -4e+04 | 9.92e+04 | -0.403 | 0.687 | -2.34e+05 | 1.54e+05 |
| industry: Payroll Software | -1.43e+04 | 9.91e+04 | -0.144 | 0.886 | -2.09e+05 | 1.8e+05 |
| industry: Pension Benefit Administration | -1.35e+04 | 9.92e+04 | -0.136 | 0.892 | -2.08e+05 | 1.81e+05 |
| industry: Per Sitter | -3.08e+04 | 9.91e+04 | -0.311 | 0.756 | -2.25e+05 | 1.64e+05 |
| industry: Performing Arts | -2.38e+04 | 9.91e+04 | -0.24 | 0.811 | -2.18e+05 | 1.71e+05 |
| industry: Pest Control | 6.19e+03 | 9.92e+04 | 0.062 | 0.95 | -1.88e+05 | 2.01e+05 |
| industry: Pet | -3.78e+04 | 9.92e+04 | -0.381 | 0.703 | -2.32e+05 | 1.57e+05 |
| industry: Pet Care | -6.93e+04 | 9.92e+04 | -0.698 | 0.485 | -2.64e+05 | 1.25e+05 |
| industry: Pet care industry | -3.24e+04 | 9.92e+04 | -0.326 | 0.744 | -2.27e+05 | 1.62e+05 |
| industry: Pet care industry (dog training/walking) | -1.29e+04 | 9.92e+04 | -0.13 | 0.897 | -2.07e+05 | 1.82e+05 |
| industry: Pet care/grooming | -5.33e+04 | 9.93e+04 | -0.537 | 0.591 | -2.48e+05 | 1.41e+05 |
| industry: Petroleum | 1.04e+05 | 9.92e+04 | 1.05 | 0.295 | -9.05e+04 | 2.98e+05 |
| industry: Pharma | 2.21e+04 | 7.24e+04 | 0.305 | 0.76 | -1.2e+05 | 1.64e+05 |
| industry: pharma / medical device design and manufacturing | 8.82e+04 | 9.95e+04 | 0.887 | 0.375 | -1.07e+05 | 2.83e+05 |
| industry: Pharma & Biotech | 3.77e+04 | 9.92e+04 | 0.38 | 0.704 | -1.57e+05 | 2.32e+05 |
| industry: Pharma R&D | 6.1e+04 | 9.92e+04 | 0.615 | 0.538 | -1.33e+05 | 2.55e+05 |
| industry: Pharma research | 5.3e+04 | 9.92e+04 | 0.534 | 0.593 | -1.41e+05 | 2.47e+05 |
| industry: Pharma/ Research | 8.71e+04 | 9.92e+04 | 0.879 | 0.38 | -1.07e+05 | 2.81e+05 |
| industry: Pharma/biotech | 6.41e+04 | 9.92e+04 | 0.647 | 0.518 | -1.3e+05 | 2.59e+05 |
| industry: Pharma/Biotech | 3.01e+04 | 9.92e+04 | 0.304 | 0.761 | -1.64e+05 | 2.24e+05 |
| industry: Pharma/biotechnology | 2.86e+04 | 8.59e+04 | 0.333 | 0.739 | -1.4e+05 | 1.97e+05 |
| industry: Pharmaceitical manufacturing | 5.55e+03 | 9.92e+04 | 0.056 | 0.955 | -1.89e+05 | 2e+05 |
| industry: pharmaceutical | 2.4e+04 | 9.92e+04 | 0.242 | 0.809 | -1.7e+05 | 2.18e+05 |
| industry: Pharmaceutical | 2.83e+04 | 7.16e+04 | 0.395 | 0.693 | -1.12e+05 | 1.69e+05 |
| industry: Pharmaceutical company | 5.05e+04 | 9.92e+04 | 0.509 | 0.611 | -1.44e+05 | 2.45e+05 |
| industry: Pharmaceutical Development | 4.97e+04 | 8.1e+04 | 0.613 | 0.54 | -1.09e+05 | 2.08e+05 |
| industry: Pharmaceutical industry | 1.35e+04 | 9.92e+04 | 0.136 | 0.892 | -1.81e+05 | 2.08e+05 |
| industry: Pharmaceutical Industry | 1.14e+04 | 9.91e+04 | 0.115 | 0.909 | -1.83e+05 | 2.06e+05 |
| industry: Pharmaceutical manufacturing | -1.07e+03 | 9.91e+04 | -0.011 | 0.991 | -1.95e+05 | 1.93e+05 |
| industry: Pharmaceutical Manufacturing | -555 | 8.1e+04 | -0.007 | 0.995 | -1.59e+05 | 1.58e+05 |
| industry: Pharmaceutical R&D | 3.21e+04 | 8.1e+04 | 0.397 | 0.692 | -1.27e+05 | 1.91e+05 |
| industry: Pharmaceutical research | -3.11e+03 | 8.59e+04 | -0.036 | 0.971 | -1.71e+05 | 1.65e+05 |
| industry: Pharmaceutical Research | 1.1e+04 | 9.92e+04 | 0.111 | 0.911 | -1.83e+05 | 2.06e+05 |
| industry: Pharmaceutical research (chemist) | -4e+04 | 9.92e+04 | -0.403 | 0.687 | -2.34e+05 | 1.54e+05 |
| industry: Pharmaceutical/Biotech | 2.71e+04 | 9.92e+04 | 0.273 | 0.785 | -1.67e+05 | 2.22e+05 |
| industry: Pharmaceutical/biotechnology | 1.07e+05 | 9.92e+04 | 1.07 | 0.283 | -8.78e+04 | 3.01e+05 |
| industry: Pharmaceutical/Contract Research Organization | 3.07e+04 | 9.92e+04 | 0.31 | 0.757 | -1.64e+05 | 2.25e+05 |
| industry: pharmaceuticals | 3.25e+03 | 9.92e+04 | 0.033 | 0.974 | -1.91e+05 | 1.98e+05 |
| industry: Pharmaceuticals | 2.2e+04 | 7.17e+04 | 0.307 | 0.759 | -1.19e+05 | 1.63e+05 |
| industry: Pharmaceuticals / Biotech | 3.95e+04 | 9.93e+04 | 0.398 | 0.691 | -1.55e+05 | 2.34e+05 |
| industry: Pharmaceuticals R&D | -8.46e+03 | 9.92e+04 | -0.085 | 0.932 | -2.03e+05 | 1.86e+05 |
| industry: Pharmaceuticals/Biotechnology | 4.49e+04 | 9.92e+04 | 0.453 | 0.651 | -1.5e+05 | 2.39e+05 |
| industry: Pharmacuticals | 3.91e+04 | 9.92e+04 | 0.394 | 0.693 | -1.55e+05 | 2.33e+05 |
| industry: Philanthropy | -2.5e+03 | 7.28e+04 | -0.034 | 0.973 | -1.45e+05 | 1.4e+05 |
| industry: Physical sciences | -3.34e+04 | 9.92e+04 | -0.337 | 0.736 | -2.28e+05 | 1.61e+05 |
| industry: Policy research | 3.5e+03 | 9.92e+04 | 0.035 | 0.972 | -1.91e+05 | 1.98e+05 |
| industry: Political Campaigning | -7.81e+03 | 9.92e+04 | -0.079 | 0.937 | -2.02e+05 | 1.87e+05 |
| industry: Political Campaigns | -1.24e+04 | 8.59e+04 | -0.145 | 0.885 | -1.81e+05 | 1.56e+05 |
| industry: Political consulting | 1.61e+04 | 9.92e+04 | 0.162 | 0.871 | -1.78e+05 | 2.11e+05 |
| industry: Political Consulting | -1.96e+04 | 9.91e+04 | -0.198 | 0.843 | -2.14e+05 | 1.75e+05 |
| industry: Political Research | -3.39e+04 | 9.92e+04 | -0.342 | 0.733 | -2.28e+05 | 1.61e+05 |
| industry: Politics | 7.23e+03 | 7.36e+04 | 0.098 | 0.922 | -1.37e+05 | 1.51e+05 |
| industry: Politics/campaigns | -4.22e+04 | 9.92e+04 | -0.425 | 0.671 | -2.37e+05 | 1.52e+05 |
| industry: Politics/Campaigns | -9.5e+03 | 9.92e+04 | -0.096 | 0.924 | -2.04e+05 | 1.85e+05 |
| industry: Politics/Government Relations | 2.36e+04 | 9.92e+04 | 0.238 | 0.812 | -1.71e+05 | 2.18e+05 |
| industry: Pre-primary education | -2.81e+04 | 9.92e+04 | -0.283 | 0.777 | -2.22e+05 | 1.66e+05 |
| industry: Preclinical Research | -1.13e+04 | 9.92e+04 | -0.114 | 0.909 | -2.06e+05 | 1.83e+05 |
| industry: Print / Mail | -1.57e+04 | 9.92e+04 | -0.158 | 0.874 | -2.1e+05 | 1.79e+05 |
| industry: Print Distributor | -1.24e+04 | 9.92e+04 | -0.125 | 0.901 | -2.07e+05 | 1.82e+05 |
| industry: Print publishing | -3.42e+04 | 9.92e+04 | -0.345 | 0.73 | -2.29e+05 | 1.6e+05 |
| industry: Printing | -4.88e+04 | 8.59e+04 | -0.568 | 0.57 | -2.17e+05 | 1.2e+05 |
| industry: Private company, federal contractor | -4.2e+04 | 9.92e+04 | -0.423 | 0.672 | -2.36e+05 | 1.52e+05 |
| industry: Private Equity | 2.19e+04 | 8.59e+04 | 0.255 | 0.799 | -1.46e+05 | 1.9e+05 |
| industry: Private Sector Emergency Management | 1.1e+04 | 9.92e+04 | 0.111 | 0.912 | -1.83e+05 | 2.05e+05 |
| industry: Procurement | -4.73e+04 | 9.92e+04 | -0.477 | 0.634 | -2.42e+05 | 1.47e+05 |
| industry: Procurement/Sourcing/Operations | 1.84e+04 | 9.91e+04 | 0.185 | 0.853 | -1.76e+05 | 2.13e+05 |
| industry: Product Management | 3.26e+04 | 9.92e+04 | 0.329 | 0.742 | -1.62e+05 | 2.27e+05 |
| industry: Production and Manufacturing | -9.86e+03 | 9.92e+04 | -0.099 | 0.921 | -2.04e+05 | 1.85e+05 |
| industry: Professional Association | -8.77e+03 | 9.91e+04 | -0.088 | 0.93 | -2.03e+05 | 1.86e+05 |
| industry: Professional Association in Finance | 2.17e+04 | 9.94e+04 | 0.219 | 0.827 | -1.73e+05 | 2.17e+05 |
| industry: Professional Public Librarian | -6.73e+04 | 9.92e+04 | -0.678 | 0.498 | -2.62e+05 | 1.27e+05 |
| industry: Professional services | 1.02e+04 | 8.59e+04 | 0.119 | 0.905 | -1.58e+05 | 1.79e+05 |
| industry: Professional Services | 6.27e+04 | 8.59e+04 | 0.73 | 0.465 | -1.06e+05 | 2.31e+05 |
| industry: Professional services / architecture | 2.22e+04 | 9.92e+04 | 0.223 | 0.823 | -1.72e+05 | 2.17e+05 |
| industry: Program management | -5.09e+03 | 9.92e+04 | -0.051 | 0.959 | -1.99e+05 | 1.89e+05 |
| industry: project management | -1.6e+04 | 8.59e+04 | -0.186 | 0.853 | -1.84e+05 | 1.52e+05 |
| industry: Property Management | -3.4e+04 | 9.92e+04 | -0.343 | 0.732 | -2.28e+05 | 1.6e+05 |
| industry: Property or Construction | -8.67e+03 | 7.02e+04 | -0.123 | 0.902 | -1.46e+05 | 1.29e+05 |
| industry: Protective coatings | -2.98e+04 | 9.92e+04 | -0.3 | 0.764 | -2.24e+05 | 1.65e+05 |
| industry: Psychologist | -6.89e+04 | 9.92e+04 | -0.695 | 0.487 | -2.63e+05 | 1.26e+05 |
| industry: Psychology | -907 | 9.92e+04 | -0.009 | 0.993 | -1.95e+05 | 1.93e+05 |
| industry: Public affairs / PR | 2.27e+04 | 9.91e+04 | 0.229 | 0.819 | -1.72e+05 | 2.17e+05 |
| industry: public health | -2.18e+03 | 8.59e+04 | -0.025 | 0.98 | -1.7e+05 | 1.66e+05 |
| industry: Public health | -2.39e+04 | 8.59e+04 | -0.278 | 0.781 | -1.92e+05 | 1.44e+05 |
| industry: Public Health | -2.25e+04 | 7.84e+04 | -0.287 | 0.774 | -1.76e+05 | 1.31e+05 |
| industry: Public health (not medical) | -2.01e+03 | 9.92e+04 | -0.02 | 0.984 | -1.96e+05 | 1.92e+05 |
| industry: Public health in higher education | -2.11e+04 | 9.92e+04 | -0.213 | 0.832 | -2.15e+05 | 1.73e+05 |
| industry: Public Health Research | 7.13e+03 | 9.92e+04 | 0.072 | 0.943 | -1.87e+05 | 2.02e+05 |
| industry: Public Health- state level | 2.91e+03 | 9.92e+04 | 0.029 | 0.977 | -1.91e+05 | 1.97e+05 |
| industry: Public Health, local government | 2.91e+04 | 9.92e+04 | 0.294 | 0.769 | -1.65e+05 | 2.23e+05 |
| industry: Public Librarian | -3.67e+04 | 9.92e+04 | -0.37 | 0.711 | -2.31e+05 | 1.58e+05 |
| industry: public libraries | -7.1e+04 | 9.92e+04 | -0.716 | 0.474 | -2.65e+05 | 1.23e+05 |
| industry: Public libraries | -3.94e+04 | 8.59e+04 | -0.459 | 0.646 | -2.08e+05 | 1.29e+05 |
| industry: Public Libraries | -2.99e+04 | 7.2e+04 | -0.415 | 0.678 | -1.71e+05 | 1.11e+05 |
| industry: public library | -4.93e+04 | 7.68e+04 | -0.642 | 0.521 | -2e+05 | 1.01e+05 |
| industry: Public library | -3.68e+04 | 7.28e+04 | -0.505 | 0.613 | -1.79e+05 | 1.06e+05 |
| industry: Public Library | -3.85e+04 | 7.13e+04 | -0.54 | 0.589 | -1.78e+05 | 1.01e+05 |
| industry: Public LIbrary | -2.54e+04 | 9.92e+04 | -0.256 | 0.798 | -2.2e+05 | 1.69e+05 |
| industry: Public Library (might be considered Government, but that always seems an odd designation...) | -3.29e+04 | 9.92e+04 | -0.331 | 0.74 | -2.27e+05 | 1.61e+05 |
| industry: Public Library (Non-profit, but also government?) | -3.61e+04 | 9.92e+04 | -0.364 | 0.716 | -2.31e+05 | 1.58e+05 |
| industry: Public Library (technically City Govt.?) | -2.77e+04 | 9.92e+04 | -0.279 | 0.78 | -2.22e+05 | 1.67e+05 |
| industry: Public Opinion Research | -2.09e+04 | 9.92e+04 | -0.211 | 0.833 | -2.15e+05 | 1.73e+05 |
| industry: Public Policy | -1.58e+04 | 8.59e+04 | -0.184 | 0.854 | -1.84e+05 | 1.53e+05 |
| industry: Public safety | -724 | 8.59e+04 | -0.008 | 0.993 | -1.69e+05 | 1.68e+05 |
| industry: Public/Environmental Health | -1.13e+04 | 9.93e+04 | -0.114 | 0.909 | -2.06e+05 | 1.83e+05 |
| industry: Public/Research Library | -3.85e+04 | 9.92e+04 | -0.388 | 0.698 | -2.33e+05 | 1.56e+05 |
| industry: Publications | -1.55e+04 | 9.92e+04 | -0.157 | 0.876 | -2.1e+05 | 1.79e+05 |
| industry: publishing | -1.98e+04 | 8.1e+04 | -0.244 | 0.807 | -1.79e+05 | 1.39e+05 |
| industry: Publishing | -1.96e+04 | 7.08e+04 | -0.277 | 0.782 | -1.58e+05 | 1.19e+05 |
| industry: Publishing (academic) | -6.89e+04 | 9.92e+04 | -0.695 | 0.487 | -2.63e+05 | 1.25e+05 |
| industry: Publishing, content as a service | -2.71e+04 | 9.91e+04 | -0.274 | 0.784 | -2.21e+05 | 1.67e+05 |
| industry: Publishing: Science, Academic, Technical | -5.36e+03 | 9.92e+04 | -0.054 | 0.957 | -2e+05 | 1.89e+05 |
| industry: publishing/edtech | -1.79e+04 | 9.92e+04 | -0.18 | 0.857 | -2.12e+05 | 1.77e+05 |
| industry: Purchasing | 1.38e+04 | 8.59e+04 | 0.161 | 0.872 | -1.55e+05 | 1.82e+05 |
| industry: R&D | 5.35e+04 | 9.92e+04 | 0.539 | 0.59 | -1.41e+05 | 2.48e+05 |
| industry: R&D in Manufacturing | -5.24e+03 | 9.93e+04 | -0.053 | 0.958 | -2e+05 | 1.89e+05 |
| industry: real estate | -1.68e+04 | 8.59e+04 | -0.195 | 0.845 | -1.85e+05 | 1.52e+05 |
| industry: Real estate | -2.26e+03 | 8.59e+04 | -0.026 | 0.979 | -1.71e+05 | 1.66e+05 |
| industry: Real Estate | -9.56e+03 | 7.19e+04 | -0.133 | 0.894 | -1.51e+05 | 1.31e+05 |
| industry: Real Estate Affordable Housing | -436 | 1e+05 | -0.004 | 0.997 | -1.97e+05 | 1.96e+05 |
| industry: Real Estate Association | -1.83e+04 | 9.91e+04 | -0.184 | 0.854 | -2.13e+05 | 1.76e+05 |
| industry: Real Estate Corp. Office/not a Realtor | -3.83e+04 | 9.92e+04 | -0.386 | 0.699 | -2.33e+05 | 1.56e+05 |
| industry: Real Estate customer care | -3.49e+04 | 9.92e+04 | -0.352 | 0.725 | -2.29e+05 | 1.59e+05 |
| industry: Real Estate Development | 5.71e+04 | 9.92e+04 | 0.576 | 0.564 | -1.37e+05 | 2.51e+05 |
| industry: Real Estate Investment | -1.6e+03 | 9.91e+04 | -0.016 | 0.987 | -1.96e+05 | 1.93e+05 |
| industry: Real Estate Investment Support | 9.17e+03 | 9.92e+04 | 0.092 | 0.926 | -1.85e+05 | 2.04e+05 |
| industry: Real estate servicea | -2.81e+04 | 9.92e+04 | -0.283 | 0.777 | -2.23e+05 | 1.66e+05 |
| industry: Real estate services | -4.06e+04 | 9.92e+04 | -0.409 | 0.682 | -2.35e+05 | 1.54e+05 |
| industry: Real estate software | -1.66e+03 | 9.92e+04 | -0.017 | 0.987 | -1.96e+05 | 1.93e+05 |
| industry: Real Estate Title Company | -2.44e+04 | 9.93e+04 | -0.246 | 0.806 | -2.19e+05 | 1.7e+05 |
| industry: Real estate valuation | 1.29e+04 | 9.92e+04 | 0.13 | 0.896 | -1.81e+05 | 2.07e+05 |
| industry: Real estate: title & escrow | -7.68e+03 | 9.93e+04 | -0.077 | 0.938 | -2.02e+05 | 1.87e+05 |
| industry: Real Estate/ Mortgage | -2.05e+04 | 9.92e+04 | -0.206 | 0.837 | -2.15e+05 | 1.74e+05 |
| industry: Real Estate/Development | -9.86e+03 | 9.92e+04 | -0.099 | 0.921 | -2.04e+05 | 1.85e+05 |
| industry: Real World Evidence (data for pharma research) | 5.93e+04 | 9.92e+04 | 0.598 | 0.55 | -1.35e+05 | 2.54e+05 |
| industry: Recruitment or HR | -27.3 | 7.02e+04 | 0 | 1 | -1.38e+05 | 1.38e+05 |
| industry: Regulatory Affairs- nutraceuticals | -2.33e+04 | 9.93e+04 | -0.235 | 0.814 | -2.18e+05 | 1.71e+05 |
| industry: Religion | -5.35e+04 | 8.59e+04 | -0.623 | 0.533 | -2.22e+05 | 1.15e+05 |
| industry: Religion/church | -4.41e+04 | 9.92e+04 | -0.445 | 0.656 | -2.39e+05 | 1.5e+05 |
| industry: Religious | -4.97e+04 | 9.92e+04 | -0.501 | 0.616 | -2.44e+05 | 1.45e+05 |
| industry: Religious (church) | -5.33e+04 | 9.92e+04 | -0.537 | 0.591 | -2.48e+05 | 1.41e+05 |
| industry: Religious (synagogue) | -2.87e+03 | 9.91e+04 | -0.029 | 0.977 | -1.97e+05 | 1.91e+05 |
| industry: religious educator | -6.21e+04 | 9.92e+04 | -0.626 | 0.531 | -2.56e+05 | 1.32e+05 |
| industry: Religious institute | -4.29e+04 | 9.92e+04 | -0.432 | 0.666 | -2.37e+05 | 1.52e+05 |
| industry: Religious institution | -4.12e+04 | 9.92e+04 | -0.416 | 0.678 | -2.36e+05 | 1.53e+05 |
| industry: Renewable energy | -841 | 8.1e+04 | -0.01 | 0.992 | -1.6e+05 | 1.58e+05 |
| industry: Renewable Energy | 9.48e+03 | 7.85e+04 | 0.121 | 0.904 | -1.44e+05 | 1.63e+05 |
| industry: Renewable Energy Development | 9.01e+03 | 9.92e+04 | 0.091 | 0.928 | -1.85e+05 | 2.03e+05 |
| industry: Repair facility for heavy duty trucks | 1.94e+03 | 9.94e+04 | 0.02 | 0.984 | -1.93e+05 | 1.97e+05 |
| industry: research | -1.31e+04 | 9.92e+04 | -0.132 | 0.895 | -2.07e+05 | 1.81e+05 |
| industry: Research | -1.44e+04 | 7.14e+04 | -0.202 | 0.84 | -1.54e+05 | 1.26e+05 |
| industry: Research - academic | -4.49e+04 | 9.92e+04 | -0.452 | 0.651 | -2.39e+05 | 1.5e+05 |
| industry: Research - Public Health | -9.67e+03 | 9.92e+04 | -0.098 | 0.922 | -2.04e+05 | 1.85e+05 |
| industry: Research & Development | 605 | 7.68e+04 | 0.008 | 0.994 | -1.5e+05 | 1.51e+05 |
| industry: Research & Development (Defense Industry) | 2.06e+04 | 9.95e+04 | 0.207 | 0.836 | -1.74e+05 | 2.16e+05 |
| industry: Research & Development (Physical Sciences) | 425 | 9.92e+04 | 0.004 | 0.997 | -1.94e+05 | 1.95e+05 |
| industry: Research Administration | -4.49e+04 | 9.92e+04 | -0.453 | 0.651 | -2.39e+05 | 1.49e+05 |
| industry: Research and development | -2.3e+04 | 9.93e+04 | -0.232 | 0.817 | -2.18e+05 | 1.72e+05 |
| industry: Research and Development | 1.86e+04 | 8.1e+04 | 0.229 | 0.819 | -1.4e+05 | 1.77e+05 |
| industry: Research and Development Academia | -2.34e+04 | 9.92e+04 | -0.236 | 0.814 | -2.18e+05 | 1.71e+05 |
| industry: Research and Development, Food and Beverage | -4.51e+04 | 9.92e+04 | -0.455 | 0.649 | -2.39e+05 | 1.49e+05 |
| industry: Research and Evaluation | -5.91e+03 | 9.92e+04 | -0.06 | 0.953 | -2e+05 | 1.88e+05 |
| industry: Research at a National Laboratory | -2.11e+03 | 9.92e+04 | -0.021 | 0.983 | -1.97e+05 | 1.92e+05 |
| industry: Research at a State University | -2.96e+04 | 9.91e+04 | -0.299 | 0.765 | -2.24e+05 | 1.65e+05 |
| industry: Research Science | -1.96e+04 | 8.59e+04 | -0.229 | 0.819 | -1.88e+05 | 1.49e+05 |
| industry: Research Scientist, Pharma | 5.9e+04 | 9.92e+04 | 0.595 | 0.552 | -1.35e+05 | 2.53e+05 |
| industry: Research/Social Science | 8.47e+03 | 9.92e+04 | 0.085 | 0.932 | -1.86e+05 | 2.03e+05 |
| industry: Restaurant | -4.15e+03 | 7.85e+04 | -0.053 | 0.958 | -1.58e+05 | 1.5e+05 |
| industry: Restaurant Group | -2.78e+04 | 9.92e+04 | -0.28 | 0.78 | -2.22e+05 | 1.67e+05 |
| industry: Restaurant/Food Service | -1.37e+04 | 9.92e+04 | -0.138 | 0.89 | -2.08e+05 | 1.81e+05 |
| industry: Restaurant/Service | 3.21e+04 | 9.92e+04 | 0.324 | 0.746 | -1.62e+05 | 2.27e+05 |
| industry: Restaurants & Hospitality | 3.61e+04 | 9.92e+04 | 0.364 | 0.716 | -1.58e+05 | 2.31e+05 |
| industry: restoration | 9.94e+04 | 9.91e+04 | 1 | 0.316 | -9.5e+04 | 2.94e+05 |
| industry: Retail | -1.65e+04 | 7.02e+04 | -0.235 | 0.814 | -1.54e+05 | 1.21e+05 |
| industry: Retail call center | -2.99e+04 | 9.92e+04 | -0.302 | 0.763 | -2.24e+05 | 1.64e+05 |
| industry: Retail mid level management | -3.47e+04 | 9.97e+04 | -0.348 | 0.728 | -2.3e+05 | 1.61e+05 |
| industry: Retail pharmacy | 4.79e+03 | 9.92e+04 | 0.048 | 0.961 | -1.9e+05 | 1.99e+05 |
| industry: Retail real estate | -4.29e+04 | 9.92e+04 | -0.432 | 0.665 | -2.37e+05 | 1.52e+05 |
| industry: Retired | -9.58e+04 | 9.96e+04 | -0.962 | 0.336 | -2.91e+05 | 9.94e+04 |
| industry: Saas | 5.7e+03 | 9.92e+04 | 0.057 | 0.954 | -1.89e+05 | 2e+05 |
| industry: Sailing Instructor | 3.9e+03 | 9.94e+04 | 0.039 | 0.969 | -1.91e+05 | 1.99e+05 |
| industry: Sales | 2e+04 | 7.03e+04 | 0.284 | 0.776 | -1.18e+05 | 1.58e+05 |
| industry: Sales operations | 1.11e+03 | 9.92e+04 | 0.011 | 0.991 | -1.93e+05 | 1.96e+05 |
| industry: Sales Operations | -682 | 8.59e+04 | -0.008 | 0.994 | -1.69e+05 | 1.68e+05 |
| industry: School District Pre-K-12 | -5.77e+04 | 9.92e+04 | -0.582 | 0.561 | -2.52e+05 | 1.37e+05 |
| industry: Science | -1.09e+04 | 7.39e+04 | -0.148 | 0.883 | -1.56e+05 | 1.34e+05 |
| industry: Science (Laboratory) | 9.93e+03 | 9.92e+04 | 0.1 | 0.92 | -1.84e+05 | 2.04e+05 |
| industry: Science (Research, Biology) | -6.59e+04 | 9.92e+04 | -0.664 | 0.506 | -2.6e+05 | 1.28e+05 |
| industry: Science academia | -3.78e+04 | 8.59e+04 | -0.44 | 0.66 | -2.06e+05 | 1.31e+05 |
| industry: Science publishing | -1.54e+04 | 9.92e+04 | -0.155 | 0.877 | -2.1e+05 | 1.79e+05 |
| industry: science research | -3.31e+04 | 9.92e+04 | -0.334 | 0.739 | -2.28e+05 | 1.61e+05 |
| industry: Science research | -1.22e+04 | 8.59e+04 | -0.142 | 0.887 | -1.81e+05 | 1.56e+05 |
| industry: Science Research | -3.07e+04 | 9.92e+04 | -0.309 | 0.757 | -2.25e+05 | 1.64e+05 |
| industry: Science Research, government | 3.98e+03 | 9.92e+04 | 0.04 | 0.968 | -1.9e+05 | 1.98e+05 |
| industry: Science/government | -1.04e+04 | 9.92e+04 | -0.105 | 0.916 | -2.05e+05 | 1.84e+05 |
| industry: Science/Research | -1.81e+04 | 9.92e+04 | -0.182 | 0.855 | -2.12e+05 | 1.76e+05 |
| industry: Science/Research (Academia) | -1.03e+04 | 9.92e+04 | -0.104 | 0.917 | -2.05e+05 | 1.84e+05 |
| industry: Science/research non-academic | -4.49e+04 | 9.92e+04 | -0.453 | 0.651 | -2.39e+05 | 1.49e+05 |
| industry: Sciences | -2.98e+04 | 9.91e+04 | -0.3 | 0.764 | -2.24e+05 | 1.65e+05 |
| industry: Scientific | 3.14e+03 | 8.59e+04 | 0.037 | 0.971 | -1.65e+05 | 1.71e+05 |
| industry: Scientific analysis | -2.53e+04 | 9.92e+04 | -0.255 | 0.799 | -2.2e+05 | 1.69e+05 |
| industry: Scientific R&D | -3.98e+04 | 9.92e+04 | -0.401 | 0.688 | -2.34e+05 | 1.55e+05 |
| industry: scientific research | -3.12e+04 | 8.59e+04 | -0.363 | 0.716 | -2e+05 | 1.37e+05 |
| industry: Scientific research | -2.17e+04 | 7.84e+04 | -0.277 | 0.782 | -1.75e+05 | 1.32e+05 |
| industry: Scientific Research | -1.09e+04 | 7.3e+04 | -0.149 | 0.882 | -1.54e+05 | 1.32e+05 |
| industry: Scientific research (industry) | 1.09e+03 | 9.92e+04 | 0.011 | 0.991 | -1.93e+05 | 1.95e+05 |
| industry: Scientist | 1.33e+04 | 8.1e+04 | 0.164 | 0.87 | -1.45e+05 | 1.72e+05 |
| industry: Security | -4.85e+03 | 8.59e+04 | -0.056 | 0.955 | -1.73e+05 | 1.64e+05 |
| industry: Service | -1.07e+04 | 9.92e+04 | -0.108 | 0.914 | -2.05e+05 | 1.84e+05 |
| industry: Service and repair | -2.26e+04 | 9.92e+04 | -0.228 | 0.82 | -2.17e+05 | 1.72e+05 |
| industry: Shared office space | -5.48e+04 | 9.92e+04 | -0.552 | 0.581 | -2.49e+05 | 1.4e+05 |
| industry: Sign Language Interpreter, Community | 3.15e+04 | 9.92e+04 | 0.318 | 0.751 | -1.63e+05 | 2.26e+05 |
| industry: Small business/service company | -3.23e+03 | 9.92e+04 | -0.033 | 0.974 | -1.98e+05 | 1.91e+05 |
| industry: Social science | 8.09e+03 | 9.92e+04 | 0.082 | 0.935 | -1.86e+05 | 2.02e+05 |
| industry: social science research - not quite academia, not quite nonprofit, not quite consulting | -6.87e+03 | 9.92e+04 | -0.069 | 0.945 | -2.01e+05 | 1.87e+05 |
| industry: Social sciences research | -846 | 9.92e+04 | -0.009 | 0.993 | -1.95e+05 | 1.94e+05 |
| industry: Social Work | -2.81e+04 | 7.03e+04 | -0.4 | 0.689 | -1.66e+05 | 1.1e+05 |
| industry: Software | 4.61e+04 | 7.57e+04 | 0.609 | 0.542 | -1.02e+05 | 1.95e+05 |
| industry: Software as a Service SaaS | -1.41e+04 | 9.92e+04 | -0.142 | 0.887 | -2.08e+05 | 1.8e+05 |
| industry: Software Development | 5.42e+04 | 9.92e+04 | 0.547 | 0.584 | -1.4e+05 | 2.49e+05 |
| industry: Software Products | 1.99e+04 | 9.92e+04 | 0.201 | 0.841 | -1.74e+05 | 2.14e+05 |
| industry: Software/programming | 1.16e+05 | 9.92e+04 | 1.17 | 0.24 | -7.8e+04 | 3.11e+05 |
| industry: Sourcing & Procurement | -1.86e+04 | 9.92e+04 | -0.187 | 0.851 | -2.13e+05 | 1.76e+05 |
| industry: Special Collections Library | -5.94e+04 | 9.92e+04 | -0.599 | 0.549 | -2.54e+05 | 1.35e+05 |
| industry: Sports | 2.07e+05 | 9.92e+04 | 2.09 | 0.037 | 1.27e+04 | 4.01e+05 |
| industry: Staffing & workforce solutions | -2.47e+04 | 9.91e+04 | -0.25 | 0.803 | -2.19e+05 | 1.7e+05 |
| industry: Staffing agency | -2.57e+04 | 9.92e+04 | -0.259 | 0.796 | -2.2e+05 | 1.69e+05 |
| industry: Staffing Firm | 3.8e+04 | 9.92e+04 | 0.383 | 0.702 | -1.56e+05 | 2.32e+05 |
| industry: Staffing Industrry | -5.59e+04 | 9.92e+04 | -0.564 | 0.573 | -2.5e+05 | 1.38e+05 |
| industry: State and federal contractor | -9.64e+03 | 9.91e+04 | -0.097 | 0.923 | -2.04e+05 | 1.85e+05 |
| industry: State DOT | -4.66e+04 | 9.92e+04 | -0.47 | 0.638 | -2.41e+05 | 1.48e+05 |
| industry: State-level public transportation agency | -1.81e+04 | 9.92e+04 | -0.182 | 0.856 | -2.13e+05 | 1.76e+05 |
| industry: Stay-at-home parent | -1.07e+05 | 9.92e+04 | -1.07 | 0.283 | -3.01e+05 | 8.78e+04 |
| industry: STEM medical research | -3.49e+04 | 9.92e+04 | -0.352 | 0.725 | -2.29e+05 | 1.6e+05 |
| industry: STEM research | 1.66e+04 | 9.92e+04 | 0.168 | 0.867 | -1.78e+05 | 2.11e+05 |
| industry: Storage Facility | 5.47e+04 | 9.91e+04 | 0.552 | 0.581 | -1.4e+05 | 2.49e+05 |
| industry: Strategy | 6.81e+04 | 9.92e+04 | 0.687 | 0.492 | -1.26e+05 | 2.63e+05 |
| industry: strategy consulting | 9.06e+04 | 9.92e+04 | 0.913 | 0.361 | -1.04e+05 | 2.85e+05 |
| industry: student | -5.94e+04 | 9.92e+04 | -0.598 | 0.55 | -2.54e+05 | 1.35e+05 |
| industry: Summer camp | -2.29e+04 | 9.92e+04 | -0.231 | 0.818 | -2.17e+05 | 1.72e+05 |
| industry: Supply chain | -7.11e+03 | 8.59e+04 | -0.083 | 0.934 | -1.75e+05 | 1.61e+05 |
| industry: Supply Chain | 2.15e+04 | 8.59e+04 | 0.25 | 0.802 | -1.47e+05 | 1.9e+05 |
| industry: Supply Chain Distribution | -2.63e+04 | 9.92e+04 | -0.265 | 0.791 | -2.21e+05 | 1.68e+05 |
| industry: Supply Chain! | 5.39e+04 | 9.92e+04 | 0.544 | 0.587 | -1.4e+05 | 2.48e+05 |
| industry: Survey methodology | -7.5e+03 | 9.92e+04 | -0.076 | 0.94 | -2.02e+05 | 1.87e+05 |
| industry: Survey Research/Public Policy Research | 1.07e+04 | 9.91e+04 | 0.108 | 0.914 | -1.84e+05 | 2.05e+05 |
| industry: Synthetic Chemical Manufacturing | -6.54e+03 | 9.92e+04 | -0.066 | 0.947 | -2.01e+05 | 1.88e+05 |
| industry: Tabletop Games Publishing | -6.68e+04 | 9.92e+04 | -0.674 | 0.501 | -2.61e+05 | 1.28e+05 |
| industry: Tailoring Service | -3.95e+04 | 9.92e+04 | -0.398 | 0.69 | -2.34e+05 | 1.55e+05 |
| industry: Tech | 5.59e+04 | 8.1e+04 | 0.69 | 0.49 | -1.03e+05 | 2.15e+05 |
| industry: Technical writing | -1.2e+04 | 9.92e+04 | -0.121 | 0.904 | -2.06e+05 | 1.82e+05 |
| industry: Technical/Cybersecurity | 8.6e+04 | 9.92e+04 | 0.867 | 0.386 | -1.08e+05 | 2.8e+05 |
| industry: Technical/IT | 1.14e+04 | 9.92e+04 | 0.115 | 0.909 | -1.83e+05 | 2.06e+05 |
| industry: Technology | 1.84e+04 | 7.84e+04 | 0.234 | 0.815 | -1.35e+05 | 1.72e+05 |
| industry: technology/SaaS | 1.26e+04 | 9.92e+04 | 0.127 | 0.899 | -1.82e+05 | 2.07e+05 |
| industry: Telecommunications | 2.3e+04 | 9.92e+04 | 0.232 | 0.816 | -1.71e+05 | 2.17e+05 |
| industry: Telecommunications (GPS) | -138 | 9.91e+04 | -0.001 | 0.999 | -1.94e+05 | 1.94e+05 |
| industry: Textbook Copyeditor | -2.71e+04 | 9.92e+04 | -0.274 | 0.784 | -2.21e+05 | 1.67e+05 |
| industry: Think tank | -1.5e+04 | 8.59e+04 | -0.175 | 0.861 | -1.83e+05 | 1.53e+05 |
| industry: Title/Real Estate | -2.39e+04 | 9.92e+04 | -0.241 | 0.81 | -2.18e+05 | 1.71e+05 |
| industry: Tourism | -4.83e+04 | 9.92e+04 | -0.487 | 0.626 | -2.43e+05 | 1.46e+05 |
| industry: Toxicology | -2.76e+04 | 9.91e+04 | -0.279 | 0.78 | -2.22e+05 | 1.67e+05 |
| industry: trade association | 6.8e+04 | 9.92e+04 | 0.685 | 0.493 | -1.26e+05 | 2.62e+05 |
| industry: Trade association | 9.42e+04 | 9.92e+04 | 0.95 | 0.342 | -1e+05 | 2.89e+05 |
| industry: Trade Association | -2.67e+03 | 7.84e+04 | -0.034 | 0.973 | -1.56e+05 | 1.51e+05 |
| industry: trade association/membership | -9.76e+03 | 9.92e+04 | -0.098 | 0.922 | -2.04e+05 | 1.85e+05 |
| industry: Trade Associations | 1.73e+03 | 9.91e+04 | 0.017 | 0.986 | -1.93e+05 | 1.96e+05 |
| industry: Training | -5.46e+03 | 8.1e+04 | -0.067 | 0.946 | -1.64e+05 | 1.53e+05 |
| industry: Training and Professional Services | -2.21e+04 | 9.92e+04 | -0.222 | 0.824 | -2.16e+05 | 1.72e+05 |
| industry: Translation | -7.25e+03 | 8.1e+04 | -0.09 | 0.929 | -1.66e+05 | 1.51e+05 |
| industry: Translation and Localization | -2.68e+04 | 9.92e+04 | -0.27 | 0.787 | -2.21e+05 | 1.68e+05 |
| industry: Translation/transcription | -6.49e+04 | 9.92e+04 | -0.655 | 0.513 | -2.59e+05 | 1.29e+05 |
| industry: Transport or Logistics | -4.17e+03 | 7.03e+04 | -0.059 | 0.953 | -1.42e+05 | 1.34e+05 |
| industry: Travel | 4.71e+04 | 9.92e+04 | 0.475 | 0.635 | -1.47e+05 | 2.41e+05 |
| industry: Union/political organizing | 4.25e+04 | 9.92e+04 | 0.428 | 0.668 | -1.52e+05 | 2.37e+05 |
| industry: Unions | -7.09e+04 | 9.92e+04 | -0.715 | 0.474 | -2.65e+05 | 1.23e+05 |
| industry: University libraries | -2.62e+04 | 9.92e+04 | -0.265 | 0.791 | -2.21e+05 | 1.68e+05 |
| industry: University research | -7.97e+04 | 1.04e+05 | -0.766 | 0.444 | -2.84e+05 | 1.24e+05 |
| industry: University tech transfer (higher ed/marketing/writing) | -1.98e+04 | 9.92e+04 | -0.2 | 0.841 | -2.14e+05 | 1.75e+05 |
| industry: Urban Planning | -2.41e+04 | 8.59e+04 | -0.281 | 0.779 | -1.92e+05 | 1.44e+05 |
| industry: User Experience (UX) Research | 3.61e+04 | 9.92e+04 | 0.364 | 0.716 | -1.58e+05 | 2.3e+05 |
| industry: Utilities & Telecommunications | 4.53e+03 | 7.03e+04 | 0.065 | 0.949 | -1.33e+05 | 1.42e+05 |
| industry: UX Research | 3.29e+04 | 9.92e+04 | 0.332 | 0.74 | -1.61e+05 | 2.27e+05 |
| industry: Veterinarian | -6.49e+04 | 9.92e+04 | -0.655 | 0.513 | -2.59e+05 | 1.29e+05 |
| industry: Veterinary | -2.65e+04 | 7.32e+04 | -0.362 | 0.717 | -1.7e+05 | 1.17e+05 |
| industry: veterinary biotech | 1.05e+04 | 9.92e+04 | 0.106 | 0.916 | -1.84e+05 | 2.05e+05 |
| industry: Veterinary care | -1.21e+04 | 9.92e+04 | -0.122 | 0.903 | -2.06e+05 | 1.82e+05 |
| industry: Veterinary Care | -2.65e+04 | 9.92e+04 | -0.267 | 0.79 | -2.21e+05 | 1.68e+05 |
| industry: Veterinary Diagnostics | -3.06e+04 | 9.91e+04 | -0.309 | 0.758 | -2.25e+05 | 1.64e+05 |
| industry: Veterinary m&a | -2.59e+04 | 9.92e+04 | -0.261 | 0.794 | -2.2e+05 | 1.69e+05 |
| industry: Veterinary medicine | -2.54e+04 | 7.58e+04 | -0.335 | 0.738 | -1.74e+05 | 1.23e+05 |
| industry: Veterinary services | -4.58e+04 | 9.92e+04 | -0.461 | 0.644 | -2.4e+05 | 1.49e+05 |
| industry: Video Game Industry | 8.09e+03 | 9.92e+04 | 0.082 | 0.935 | -1.86e+05 | 2.03e+05 |
| industry: Video games | 6.45e+03 | 8.59e+04 | 0.075 | 0.94 | -1.62e+05 | 1.75e+05 |
| industry: Video Games | -5.54e+03 | 7.84e+04 | -0.071 | 0.944 | -1.59e+05 | 1.48e+05 |
| industry: Virtual Assisting | -5.63e+04 | 9.92e+04 | -0.567 | 0.57 | -2.51e+05 | 1.38e+05 |
| industry: Virtual reality | -1.58e+03 | 9.92e+04 | -0.016 | 0.987 | -1.96e+05 | 1.93e+05 |
| industry: Warehouse | -4.67e+04 | 8.1e+04 | -0.576 | 0.565 | -2.05e+05 | 1.12e+05 |
| industry: Warehouse- Food and Beverage | -4.01e+04 | 9.92e+04 | -0.404 | 0.686 | -2.35e+05 | 1.54e+05 |
| industry: Warehousing | -4.82e+04 | 9.92e+04 | -0.486 | 0.627 | -2.43e+05 | 1.46e+05 |
| industry: Waste and recycling | -4.96e+04 | 9.92e+04 | -0.5 | 0.617 | -2.44e+05 | 1.45e+05 |
| industry: Wealth advisor Research | -3.81e+04 | 9.92e+04 | -0.384 | 0.701 | -2.32e+05 | 1.56e+05 |
| industry: Wherever I'm assigned via the union | -3.33e+03 | 9.92e+04 | -0.034 | 0.973 | -1.98e+05 | 1.91e+05 |
| industry: Wholesale | -5.86e+03 | 9.92e+04 | -0.059 | 0.953 | -2e+05 | 1.89e+05 |
| industry: Wholesale - Apparel | -756 | 9.92e+04 | -0.008 | 0.994 | -1.95e+05 | 1.94e+05 |
| industry: wholesale distribution | -6.85e+03 | 8.1e+04 | -0.085 | 0.933 | -1.66e+05 | 1.52e+05 |
| industry: Wholesale Industrial & Welding Supplies & Equipment | -3.19e+04 | 9.96e+04 | -0.321 | 0.748 | -2.27e+05 | 1.63e+05 |
| industry: Wholesale supplier | -4.47e+04 | 9.92e+04 | -0.45 | 0.652 | -2.39e+05 | 1.5e+05 |
| industry: Wholesale textile manufacture and sales | -4.11e+04 | 9.92e+04 | -0.415 | 0.679 | -2.36e+05 | 1.53e+05 |
| industry: Wholesale Trade | -3.81e+04 | 9.92e+04 | -0.384 | 0.701 | -2.32e+05 | 1.56e+05 |
| industry: Wholesale/Distrbution | -3.35e+04 | 9.92e+04 | -0.337 | 0.736 | -2.28e+05 | 1.61e+05 |
| industry: Wine | -1.26e+04 | 9.91e+04 | -0.127 | 0.899 | -2.07e+05 | 1.82e+05 |
| industry: Wine Wholesale | 362 | 9.91e+04 | 0.004 | 0.997 | -1.94e+05 | 1.95e+05 |
| industry: Winery regulatory compliance | -2.31e+04 | 9.91e+04 | -0.233 | 0.815 | -2.17e+05 | 1.71e+05 |
| industry: Work-Study | -7.04e+04 | 9.92e+04 | -0.71 | 0.478 | -2.65e+05 | 1.24e+05 |
| industry: Workforce development | -1.71e+04 | 9.92e+04 | -0.172 | 0.863 | -2.11e+05 | 1.77e+05 |
| industry: Writing and journalism | -2.01e+04 | 9.92e+04 | -0.203 | 0.839 | -2.15e+05 | 1.74e+05 |
| industry: Zoo | -4e+04 | 8.1e+04 | -0.494 | 0.621 | -1.99e+05 | 1.19e+05 |
| industry: Zoos and Aquariums | -6.53e+04 | 9.92e+04 | -0.658 | 0.51 | -2.6e+05 | 1.29e+05 |
| highest_level_of_education_completed: High School | -2.08e+04 | 3.95e+03 | -5.26 | 0 | -2.85e+04 | -1.3e+04 |
| highest_level_of_education_completed: Master's degree | 1.32e+04 | 1.16e+03 | 11.4 | 0 | 1.09e+04 | 1.55e+04 |
| highest_level_of_education_completed: PhD | 3.63e+04 | 2.46e+03 | 14.7 | 0 | 3.14e+04 | 4.11e+04 |
| highest_level_of_education_completed: Professional degree (MD, JD, etc.) | 5.83e+04 | 2.71e+03 | 21.5 | 0 | 5.3e+04 | 6.36e+04 |
| highest_level_of_education_completed: Some college | -1.72e+04 | 2.04e+03 | -8.42 | 0 | -2.12e+04 | -1.32e+04 |
ask_lm %>%
get_regression_summaries()| r_squared | adj_r_squared | mse | rmse | sigma | statistic | p_value | df | nobs |
|---|---|---|---|---|---|---|---|---|
| 0.173 | 0.134 | 4.69e+09 | 6.85e+04 | 7.01e+04 | 4.41 | 0 | 984 | 2.18e+04 |
#Summary Stats of Salary by Gender in the USA
ask_a_manager_2021_cleaned_gender_country %>%
group_by(gender) %>%
drop_na(gender) %>%
summarise(MeanSalary=mean(salary_usd, na.rm=TRUE),
MedianSalary=median(salary_usd, na.rm=T),
sdsalary= sd(salary_usd, na.rm=TRUE),
q1salary=quantile(salary_usd, prob=.25, na.rm=TRUE),
q3salary=quantile(salary_usd, prob=.75, na.rm=TRUE),
minsalary=min(salary_usd, na.rm=T),
maxsalary=max(salary_usd, na.rm=T)) | gender | MeanSalary | MedianSalary | sdsalary | q1salary | q3salary | minsalary | maxsalary |
|---|---|---|---|---|---|---|---|
| Man | 1.17e+05 | 1.04e+05 | 7.69e+04 | 7e+04 | 1.5e+05 | 0 | 1.65e+06 |
| Non-binary | 7.94e+04 | 5.91e+04 | 1.54e+05 | 4.3e+04 | 8.6e+04 | 4e+03 | 3.6e+06 |
| Other or prefer not to answer | 9.59e+04 | 8.5e+04 | 7.48e+04 | 5.61e+04 | 1.15e+05 | 0 | 8e+05 |
| Woman | 8.75e+04 | 7.5e+04 | 6.97e+04 | 5.5e+04 | 1.05e+05 | 0 | 5e+06 |
The linear model is not the best approach to generate insights from this data. After breaking the data up into different groups and trying to explore the variables, we concluded that there isn’t a very strong linear model in the data. We could have filtered the data more in order to create a more linear model, but the more we filter the data the less people would be included. Our linear model, wıth an R squared of about 0.25 includes only __% of the data and we felt it wouldn’t be helpful to reduce the model more. Because of this we believe it will be more beneficial to explain the data using graphs and hypothesis tests.
7 Mapping Salaries to US States
#loading the libraries
library(usmap)
library(ggplot2)
#cleaning the dataset to include data only from the US and cleaning the names in the State column
ask_a_manager_states_clean <- ask_a_manager_2021_cleaned %>%
filter(country == "United States") %>%
rowwise() %>%
mutate(state = str_split(state, ",")) %>%
mutate(state = as.character(state[1]))
#creating a new dataframe with the mean salaries for each state
usmap_df <- ask_a_manager_states_clean %>%
group_by(state) %>%
summarise(median_salary = median(salary_usd, na.rm = TRUE))
#plotting the data with a color code
plot_usmap(regions = "state", data = usmap_df, values = "median_salary") +
labs(title = "Median Salary by State in the United States of America",
subtitle = "Ask a Manager Survey 2021") +
scale_fill_continuous(low = "white",
high = "red",
name = "Salary in US $",
label = scales::comma)+
theme(panel.background=element_blank(), legend.position = "right")
#TECH MAP
#loading the libraries
library(usmap)
library(ggplot2)
#cleaning the dataset to include data only from the US and cleaning the names in the State column
ask_a_manager_states_clean <- ask_a_manager_2021_cleaned %>%
filter(country == "United States") %>%
filter(industry == "Computing or Tech") %>%
rowwise() %>%
mutate(state = str_split(state, ",")) %>%
mutate(state = as.character(state[1]))
#creating a new dataframe with the mean salaries for each state
usmap_df <- ask_a_manager_states_clean %>%
group_by(state) %>%
summarise(median_salary = median(salary_usd, na.rm = TRUE))
#plotting the data with a color code
plot_usmap(regions = "state", data = usmap_df, values = "median_salary") +
labs(title = "Median Salary by State in the United States of America for the Tech Industry",
subtitle = "Ask a Manager Survey 2021") +
scale_fill_continuous(low = "white",
high = "blue",
name = "Salary in US $",
label = scales::comma)+
theme(panel.background=element_blank(), legend.position = "right")
Let’s look at the health care industry in US. It is in the top 5 popular industries in the US and it is very different from tech. So it may be interesting to see median salary in this industry.
#loading the libraries
library(usmap)
library(ggplot2)
#cleaning the dataset to include data only from the US and cleaning the names in the State column
ask_a_manager_states_clean <- ask_a_manager_2021_cleaned %>%
filter(country == "United States") %>%
filter(industry == "Health care") %>%
rowwise() %>%
mutate(state = str_split(state, ",")) %>%
mutate(state = as.character(state[1]))
#creating a new dataframe with the mean salaries for each state
usmap_df <- ask_a_manager_states_clean %>%
group_by(state) %>%
summarise(median_salary = median(salary_usd, na.rm = TRUE))
#plotting the data with a color code
plot_usmap(regions = "state", data = usmap_df, values = "median_salary") +
labs(title = "Median Salary by State in the United States of America for the Healthcare Industry",
subtitle = "Ask a Manager Survey 2021") +
scale_fill_continuous(low = "white",
high = "darkgreen",
name = "Salary in US $",
label = scales::comma)+
theme(panel.background=element_blank(), legend.position = "right")
How about Accounting, Banking and Finance?
#loading the libraries
library(usmap)
library(ggplot2)
#cleaning the dataset to include data only from the US and cleaning the names in the State column
ask_a_manager_states_clean <- ask_a_manager_2021_cleaned %>%
filter(country == "United States") %>%
filter(industry == "Accounting, Banking & Finance") %>%
rowwise() %>%
mutate(state = str_split(state, ",")) %>%
mutate(state = as.character(state[1]))
#creating a new dataframe with the mean salaries for each state
usmap_df <- ask_a_manager_states_clean %>%
group_by(state) %>%
summarise(median_salary = median(salary_usd, na.rm = TRUE))
#plotting the data with a color code
plot_usmap(regions = "state", data = usmap_df, values = "median_salary") +
labs(title = "Median Salary by State in the United States of America for the Accounting, Banking & Finance Industry",
subtitle = "Ask a Manager Survey 2021") +
scale_fill_continuous(low = "white",
high = "darkorange",
name = "Salary in US $",
label = scales::comma)+
theme(panel.background=element_blank(), legend.position = "right")
The regression isn’t so great since almost all of the variables are categorical, so we want to conclude more hypothesis testings to produce more insights about the data.